Patent Publication Number: US-2022232321-A1

Title: Systems and methods for retroactive processing and transmission of words

Description:
BACKGROUND 
     This application claims the benefit of priority of U.S. Provisional Patent Application No. 63/139,861, filed on Jan. 21, 2021. The foregoing application is incorporated herein by reference in its entirety. 
     Technical Field 
     This disclosure generally relates to devices and methods for capturing and processing images and audio from an environment of a user, and using information derived from captured images and audio. 
     Background Information 
     Today, technological advancements make it possible for wearable devices to automatically capture images and audio, and store information that is associated with the captured images and audio. Certain devices have been used to digitally record aspects and personal experiences of one&#39;s life in an exercise typically called “lifelogging.” Some individuals log their life so they can retrieve moments from past activities, for example, social events, trips, etc. Lifelogging may also have significant benefits in other fields (e.g., business, fitness and healthcare, and social research). Lifelogging devices, while useful for tracking daily activities, may be improved with capability to enhance one&#39;s interaction in his environment with feedback and other advanced functionality based on the analysts of captured image and audio data. 
     Even though users can capture images and audio with their smartphones and some smartphone applications can process tire captured information, smartphones may not be live best platform for serving as lifelogging apparatuses in view of their size and design. Lifelogging apparatuses should be small and light, so they can be easily worn. Moreover, with improvements in image capture devices, including wearable apparatuses, additional functionality may be provided to assist users in navigating in and around an environment, identifying persons and objects they encounter, and providing feedback to the users about their surroundings and activities. Therefore, there is a need for apparatuses and methods for automatically capturing and processing images and audio to provide useful information to users of the apparatuses, and for systems and methods to process and leverage information gathered by the apparatuses. 
     SUMMARY 
     Embodiments consistent with the present disclosure provide devices and methods for automatically capturing and processing images and audio from an environment of a user, and  systems and methods for processing information related to images and audio captured from the environment of the user. 
     In an embodiment, a hearing aid system for selectively transmitting audio signals may comprise at least one microphone configured to capture sounds front an environment of the user; and at least one processor. The at least one processor may be programmed to receive an audio signal representative of the sounds captured by the microphone, the audio signal being associated with an original rate; process a first sample period of the audio signal using a first engine, wherein the first simple period includes a first portion of the audio signal starting at a first point in time of the audio signal; determine, based on the processing of the first sample period of the audio signal using the first engine, that the audio signal is not to be transmitted to a bearing interface device; after determining that the audio signal is not to be transmitted to the hearing interface device based on the processing of the first sample period of the audio signal using the first engine, process a second sample period of the audio signal using a second engine, wherein the second sample period of the audio signal includes a second portion of the audio signal, the second portion of the audio signal including at least part of the first portion of the audio signal, the second portion of the audio signal ending at a second point in time of the audio signal, the second point in time being at a time delay after the first point in time; determine, based on the processing of the second sample period of the audio signal using the second engine, that the audio signal is to be transmitted to the hearing interface device; and after determining that the audio signal is to be transmitted to the hearing interface device based on the processing of the second sample period of the audio signal using the second engine, transmit at least a part of the first portion of the audio signal to the hearing interface device at an increased rate, the increased rate being faster than the original rate. 
     In another embodiment, a method for selectively transmitting audio signals is disclosed. The method may comprise processing a first sample period of the audio signal using a first engine, wherein the first sample period includes a first portion of the audio signal starting at a first point in time of the audio signal; determining, based on the processing of the first sample period of the audio signal using the first engine, that the audio signal is not to be transmitted to a hearing interface device; after determining that the audio signal is not to be transmitted to the hearing interface device based on the processing of the first sample period of the audio signal using the first engine, processing a second sample period of the audio signal using a second engine, wherein the second sample period of the audio signal includes a second portion of the audio signal, the second portion of the audio signal including at least pan of the first portion of the audio signal, the second portion of the audio signal ending at a second point in time of the audio signal, the second point in time being at a time delay after the first point in  time; determining, based on tire processing of the second sample period of the audio signal using the second engine, that the audio signal is to be transmitted to the hearing interface device; and alter determining that the audio signal is to be transmitted to the hearing interface device based on the processing of the second sample period of the audio signal using the second engine, transmitting at least a part of the first portion of the audio signal to the hearing interface device at an increased rate, the increased rale being faster than the original rate. 
     In another embodiment, a hearing aid system for selectively transmitting audio signals may comprise at least one microphone configured to capture sounds from an environment of the user; and at least one processor. The at least one processor may be programmed to receive an audio signal representative of the sounds captured by the microphone, the audio signal being associated with an original rate; process a first sample period of the audio signal using a first engine, w herein the first sample period includes a first portion of the audio signal starting at a first point in time of the audio signal; determine, based on the processing of the first sample period of the audio signal using the first engine, that the audio signal is not to be transmitted to a hearing interface device; after determining that the audio signal is not to be transmitted to the hearing interface device based on the processing of the first sample period of the audio signal using the first engine, process a second sample period of the audio signal using a second engine, wherein the second sample period of the audio signal includes a second portion of the audio signal, the second portion of the audio signal including at least part of the first portion of the audio signal, the second portion of rite audio signal ending at a second point in time of the audio signal, the second point in time being at a first time delay after the first point in time; and determine, based on the processing of the second sample period of the audio signal using the second engine, that the audio signal is not to be transmitted to the hearing interface device; alter determining that the audio signal is not to be transmitted to the hearing interlace device based on the processing of the second sample period of the audio signal using the second engine, process a third sample period of the audio signal using a third engine, wherein the third sample period of the audio signal includes a third portion of the audio signal, the third portion of the audio signal including at least part of the second portion of the audio signal and at least part of the first portion of the audio signal, the third portion of the audio signal ending at a third point in time of the audio signal, the third point in time being at a second time delay after the first point in time; determine, based on the processing of the third sample period of the audio signal using the third engine, that the audio signal is to be transmitted to the hearing interlace device; and after determining that the audio signal is to be transmitted to the hearing interface device based on the processing of the third sample period of the audio signal using the third engine,  transmit at least a part of the first portion of the audio signal to the hearing interface device at an increased rate, the increased rate being faster than the original rate 
     Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processor and perform any of the methods described herein. 
     The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings; 
         FIG. 1A  is a schematic illustration of an example of a user wearing a wearable apparatus according to a disclosed embodiment. 
         FIG. 1B  is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment. 
         FIG. 1C  is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment. 
         FIG. 1D  is a schematic illustration of an example of the user wearing a wearable apparatus according to a disclosed embodiment. 
         FIG. 2  is a schematic illustration of an example system consistent with the disclosed embodiments. 
         FIG. 3A  is a schematic illustration of an example of the wearable apparatus shown in  FIG. 1A . 
         FIG. 3B  is an exploded view of the example of the wearable apparatus shown in  FIG. 3A . 
         FIGS. 4A-4K  are schematic illustrations of an example of the wearable apparatus shown in  FIG. 1B  from various viewpoints. 
         FIG. 5A  is a block diagram illustrating an example of the components of a wearable apparatus according to a first embodiment. 
         FIG. 5B  is a block diagram illustrating an example of the components of a wearable apparatus according to a second embodiment. 
         FIG. 5C  is a block diagram illustrating an example of the components of a wearable apparatus according to a third embodiment. 
         FIG. 6  illustrates an exemplary embodiment of a memory containing software modules consistent with the present disclosure.  
         FIG. 7  is a schematic illustration of an embodiment of a wearable apparatus including an orientable image capture unit. 
         FIG. 8  is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure. 
         FIG. 9  is a schematic illustration of a user wearing a wearable apparatus consistent with an embodiment of the present disclosure. 
         FIG. 10  is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure. 
         FIG. 11  is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure. 
         FIG. 12  is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure. 
         FIG. 13  is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure. 
         FIG. 14  is a schematic illustration of an embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure. 
         FIG. 15  is a schematic illustration of an embodiment of a wearable apparatus power unit including a power source. 
         FIG. 16  is a schematic illustration of an exemplary embodiment of a wearable apparatus including protective circuitry. 
         FIG. 17A  is a schematic illustration of an example of a user wearing an apparatus for a camera-based hearing aid device according to a disclosed embodiment. 
         FIG. 17B  is a schematic illustration of an embodiment of an apparatus securable to an article of clothing consistent with the present disclosure. 
         FIG. 18  is a schematic illustration showing an exemplary environment for use of a camera-based hearing aid consistent with the present disclosure. 
         FIG. 19  is a flowchart showing an exemplary process for selectively amplifying sounds emanating from a detected look direction of a user consistent with disclosed embodiments. 
         FIG. 20A  is a schematic illustration showing an exemplary environment for use of a hearing aid with voice and/or image recognition consistent with the present disclosure. 
         FIG. 20B  illustrates an exemplary embodiment of an apparatus comprising facial and voice recognition components consistent with the present disclosure.  
         FIG. 21  is a flowchart showing an exemplary process for selectively amplifying audio signals associated with a voice of a recognized individual consistent with disclosed embodiments. 
         FIG. 22  is a flowchart showing an exemplary process for selectively transmitting audio signals associated with a voice of a recognized user consistent with disclosed embodiments. 
         FIG. 23A  is a schematic illustration showing an exemplary individual that may be identified in the environment of a user consistent with the present disclosure. 
         FIG. 23B  is a schematic illustration showing an exemplary individual that may be identified in the environment of a user consistent with the present disclosure. 
         FIG. 23C  illustrates an exemplary lip-tracking system consistent with the disclosed embodiments. 
         FIG. 24  is a schematic illustration showing an exemplary environment for use of a lip-tracking hearing aid consistent with the present disclosure. 
         FIG. 25  is a flowchart showing an exemplary process for selectively amplifying audio signals based on tracked lip movements consistent with disclosed embodiments. 
         FIG. 26  illustrates example processing engines that may be used to analyze an audio signal, consistent with the disclosed embodiments. 
         FIG. 27  illustrates an example audio signal that may be processed using multiple processing engines, consistent with the disclosed embodiments. 
         FIG. 28  is a flowchart showing an example process for selectively transmitting audio signals, consistent with the disclosed embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations and other implementations are possible. For example, substitutions, additions or modifications may be made to the components illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope is defined by the appended claims. 
       FIG. 1A  illustrates a user  100  wearing an apparatus  110  that is physically connected (or integral) to glasses  130 , consistent with the disclosed embodiments. Glasses  130  may be prescription glasses, magnifying glasses, non-prescription glasses, safety glasses,  sunglasses, etc. Additionally, in some embodiments, glasses  130  may include parts of a frame and earpieces, nosepieces, etc., and one or no lenses. Thus, in some embodiments, glasses  130  may function primarily to support apparatus  110 , and/or an augmented reality display device or other optical display device. In some embodiments, apparatus  110  may include an image sensor (not shown in  FIG. 1A ) for capturing real-time image data of the field-of-view of user  100 . The term “image data” includes any form of data retrieved from optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums. The image data may include video clips and/or photographs. 
     In some embodiments, apparatus  110  may communicate wirelessly or via a wire with a computing device  120 . In some embodiments, computing device  120  may include, for example, a smartphone, or a tablet, or a dedicated processing unit, which may be portable (e.g., can be carried in a pocket of user  100 ). Although shown in  FIG. 1A  as an external device, in some embodiments, computing device  120  may be provided as part of wearable apparatus  110  or glasses  130 , whether integral thereto or mounted thereon. In some embodiments, computing device  120  may be included in an augmented real ire display device or optical head mounted display provided integrally or mounted to glasses  130 . In other embodiments, computing device  120  may be provided as part of another wearable or portable apparatus of user  100  including a wrist-strap, a multifunctional watch, a button, a clip-on, etc. And in other embodiments, computing device  120  may be provided as part of another system, such as an on-board automobile computing or navigation system. A person skilled in the art can appreciate that different types of computing devices and arrangements of devices may implement the functionality of the disclosed embodiments. Accordingly, in other implementations, computing device  120  may include a Personal Computer (PC), laptop, an Internet server, etc. 
       FIG. 1B  illustrates user  100  wearing apparatus  110  that is physically connected to a necklace  140 , consistent with a disclosed embodiment. Such a configuration of apparatus  110  may be suitable for users that do not wear glasses some or all of the time. In this embodiment, user  100  can easily wear apparatus  110 , and take it off. 
       FIG. 1C  illustrates user  100  wearing apparatus  110  that is physically connected to a belt  150 , consistent with a disclosed embodiment. Such a configuration of apparatus  110  may be designed as a bell buckle. Alternatively, apparatus  110  may include a clip for attaching to various clothing articles, such as belt  150 , or a vest, a pocket, a collar, a cap or hat or other portion of a clothing article. 
       FIG. 1D  illustrates user  100  wearing apparatus  110  that is physically connected to a wrist strap  160 , consistent with a disclosed embodiment. Although the aiming direction of apparatus  110 , according to this embodiment, may not match the field-of-view of user  100 ,  apparatus  110  may include the ability to identify a hand-related trigger based on the tracked eye movement of a user  100  indicating that user  100  is looking in the direction of the wrist strap  160 . Wrist strap  160  may also include an accelerometer, a gyroscope, or other sensor for determining movement or orientation of a user&#39;s  100  hand for identifying a hand-related trigger. 
       FIG. 2  is a schematic illustration of an exemplary system  200  including a wearable apparatus  110 , worn by user  100 , and an optional computing device  120  and/or a server  250  capable of communicating with apparatus  110  via a network  240 , consistent with disclosed embodiments. In some embodiments, apparatus  110  may capture and analyze image data, identify a hand-related trigger present in the image data, and perform an action and/or provide feedback to a user  100 , based at least in part on the identification of the hand-related trigger. In some embodiments, optional computing device  120  and/or server  250  may provide additional functionality to enhance interactions of user  100  with his or her environment, as described in greater detail below. 
     According to the disclosed embodiments, apparatus  110  may include an image sensor system  220  for capturing real-time image data of the field-of-view of user  100 . In some embodiments, apparatus  110  may also include a processing unit  210  for controlling and performing the disclosed functionality of apparatus  110 , such as to control the capture of image data, analyze the image data, and perform an action and/or output a feedback based on a hand-related trigger identified in the image data. According to the disclosed embodiments, a hand-related trigger may include a gesture performed by user  100  involving a portion of a hand of user  100 . Further, consistent with some embodiments, a hand-related trigger may include a wrist-related trigger. Additionally, in some embodiments, apparatus  110  may include a feedback outputting unit  230  for producing an output of information to user  100 . 
     As discussed above, apparatus  110  may include an image sensor  220  for capturing image data. The term “image sensor” refers to a device capable of detecting and converting optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums into electrical signals. The electrical signals may be used to form an image or a video stream (i.e. image data) based on the detected signal. The term “image data” includes any form of data retrieved from optical signals in the near-infrared, infrared, visible, and ultraviolet spectrums. Examples of image sensors may include semiconductor charge-coupled devices (CCD), active pixel sensors in complementary metal oxide semiconductor (CMOS), or N-type metal-oxide-semiconductor (NMOS, Live MOS). In some cases, image sensor  220  may be part of a camera included in apparatus  110 . 
     Apparatus  110  may also include a processor  210  for controlling image sensor  220  to capture image data and for analyzing the image data according to the disclosed embodiments.  As discussed in further detail below with respect to  FIG. 5A , processor  210  may include a “processing device” for performing logic operations on one or more inputs of image data and other data according to stored or accessible software instructions providing desired functionality. In some embodiments, processor  210  may also control feedback outputting unit  230  to provide feedback to user  100  including information based on the analyzed image data and the stored software instructions. As the term is used herein, a “processing device” may access memory where executable instructions are stored or, in some embodiments, a “processing device” itself may include executable instructions (e.g., stored in memory included in the processing device). 
     In some embodiments, the information or feedback information provided to user  100  may include time information. The time information may include any information related to a current time of day and, as described further below, may be presented in any sensory perceptive manner. In some embodiments, time information may include a current time of day in a preconfigured format (e.g., 2:30 pm or 14:30). Time information may include the time in the user&#39;s current time zone (e.g., based on a determined location of user  100 ), as well as an indication of the time zone and/or a time of day in another desired location. In some embodiments, time information may include a number of hours or minutes relative to one or more predetermined times of day. For example, in some embodiments, time information may include an indication that three hours and fifteen minutes remain until a particular hour (e.g., until 6:00 pm), or some other predetermined time. Time information may also include a duration of time passed since the beginning of a particular activity, such as the start of a meeting or the start of a jog, or any other activity. In some embodiments, the activity may be determined based on analyzed image data. In other embodiments, time information may also include additional information related to a current time and one or more other routine, periodic, or scheduled events. For example, time information may include an indication of the number of minutes remaining until the next scheduled event, as may be determined from a calendar function or other information retrieved from computing device  120  or server  250 , as discussed in further detail below. 
     Feedback outputting unit  230  may include one or more feedback systems for providing the output of information to user  100 . In the disclosed embodiments, the audible or visual feedback may be provided via any typo of connected audible or visual system or both. Feedback of information according to the disclosed embodiments may include audible feedback to user  100  (e.g., using a Bluetooth™ or other wired or wirelessly connected speaker, or a bone conduction headphone). Feedback outputting unit  230  of some embodiments may additionally or alternatively produce a visible output of information to user  100 , for example, as part of an augmented reality display projected onto a lens of glasses  130  or provided via a separate heads  up display in communication with apparatus  110 , such as a display  260  provided as part of computing device  120 , which may include an onboard automobile heads up display, an augmented reality device, a virtual reality device, a smartphone, PC, table, etc. 
     The term “computing device” refers to a device including a processing unit and having computing capabilities. Some examples of computing device  120  include a PC, laptop, tablet, or other computing systems such as an on-board computing system of an automobile, for example, each configured to communicate directly with apparatus  110  or server  250  over network  240 . Another example of computing device  120  includes a smartphone having a display  260 . In some embodiments, computing device  120  may be a computing system configured particularly for apparatus  110 , and may be provided integral to apparatus  110  or tethered thereto. Apparatus  110  can also connect to computing device  120  over network  240  via any known wireless standard (e.g., Wi-Fi Bluetooth™, etc.), as well as near-tiled capacitive coupling, and other short range wireless techniques, or via a wired connection. In an embodiment in which computing device  120  is a smartphone, computing device  120  may have a dedicated application installed therein. For example, user  100  may view on display  260  data (e.g., images, video clips, extracted information, feedback information, etc.) that originate from or are triggered by apparatus  110 . In addition, user  100  may select part of the data for storage in server  250 . 
     Network  240  may be a shared, public, or private network, may encompass a wide area or local area, and may be implemented through any suitable combination of wired and or wireless communication networks. Network  240  may further comprise an intranet or the Internet. In some embodiments, network  240  may include short range or near-field wireless communication systems for enabling communication between apparatus  110  and computing device  120  provided in close proximity to each other, such as on or near a user&#39;s person, for example. Apparatus  110  may establish a connection to network  240  autonomously, for example, using a wireless module (e.g., Wi-Fi, cellular). In some embodiments, apparatus  110  may use the wireless module when being connected to an external power source, to prolong battery life. Further, communication between apparatus  110  and server  250  may be accomplished through any suitable communication channels, such as, for example, a telephone network, an extranet, an intranet, the Internet, satellite communications, off-line communications, wireless communications, transponder communications, a local area network (LAN), a wide area network (WAN), and a virtual private network (VPN). 
     As shown in  FIG. 2 , apparatus  110  may transferor receive data to/from server  250  via network  240 . In the disclosed embodiments, the data being received from server  250  and/or computing device  120  may include numerous different types of information based on the  analyzed image data, including information related to a commercial product, or a person&#39;s identity, an identified landmark, and any other information capable of being stored in or accessed by server  250 . In some embodiments, data may be received and transferred via computing device  120 . Server  250  and/or computing device  120  may retrieve information from different data sources (e.g., a user specific database or a user&#39;s social network account or other account, the Internet, and other managed or accessible databases) and provide information to apparatus  110  related to the analyzed image data and a recognized trigger according to the disclosed embodiments. In some embodiments, calendar-related information retrieved from the different data sources may be analyzed to provide certain time information or a time-based context for providing certain information based on the analyzed image data. 
     An example of wearable apparatus  110  incorporated with glasses  130  according to some embodiments (as discussed in connection with  FIG. 1A ) is shown in greater detail in  FIG. 3A . In some embodiments, apparatus  110  may be associated with a structure (not shown in  FIG. 3A ) that enables easy detaching and reattaching of apparatus  110  to glasses  130 . In some embodiments, when apparatus  110  attaches to glasses  130 , image sensor  220  acquires a set aiming direction without the need for directional calibration. The set aiming direction of image sensor  220  may substantially coincide with the field-of-view of user  100 . For example, a camera associated with image sensor  220  may be installed within apparatus  110  in a predetermined angle in a position facing slightly downwards (e.g., 5-15 degrees from the horizon). Accordingly, the set aiming direction of image sensor  220  may substantially match the field-of-view of user  100 . 
       FIG. 3B  is an exploded view of the components of the embodiment discussed regarding  FIG. 3A . Attaching apparatus  110  to glasses  130  may take place in the following way. Initially, a support  310  may be mounted on glasses  130  using a screw  320 , in the side of support  310 . Then, apparatus  110  may be clipped on support  310  such that it is aligned with the field-of-view of user  100 . The term “support” includes any device or structure that enables detaching and reattaching of a device including a camera to a pair of glasses or to another object (e.g., a helmet). Support  310  may be made from plastic (e.g., polycarbonate), metal (e.g., aluminum), or a combination of plastic and metal (e.g., carbon fiber graphite). Support  310  may be mounted on any kind of glasses (e.g., eyeglasses, sunglasses. 3D glasses, safety glasses, etc.) using screws, bolts, snaps, or any fastening means used in live art. 
     In some embodiments, support  310  may include a quick release mechanism for disengaging and reengaging apparatus  110 . For example, support  310  and apparatus  110  may include magnetic elements. As an alternative example, support  310  may include a male latch member and apparatus  110  may include a female receptacle. In other embodiments, support  310   can be an integral part of a pair of glasses, or sold separately and installed by an optometrist. For example, support  310  may be configured for mounting on the arms of glasses  130  near the frame front, but before the hinge. Alternatively, support  310  may be configured for mounting on the bridge of glasses  130 . 
     In some embodiments, apparatus  110  may be provided as part of a glasses frame  130 , with or without lenses. Additionally, in some embodiments, apparatus  110  may be configured to provide an augmented reality display projected onto a lens of glasses  130  (if provided), or alternatively, may include a display for projecting time information, for example, according to the disclosed embodiments. Apparatus  110  may include the additional display or alternatively, may be in communication with a separately provided display system that may or may not be attached to glasses  130 . 
     In some embodiments, apparatus  110  may be implemented in a form other than wearable glasses, as described above with respect to  FIGS. 1B-1D , for example.  FIG. 4A  is a schematic illustration of an example of an additional embodiment of apparatus  110  from a from viewpoint of apparatus  110 . Apparatus  110  includes an image sensor  220 , a clip (not shown), a function button (not shown) and a hanging ring  410  for attaching apparatus  110  to, for example, necklace  140 , as shown in  FIG. 1B . When apparatus  110  hangs on necklace  140 , the aiming direction of image sensor  220  may not fully coincide with the field-of-view of user  100 , but the aiming direction would still correlate with the field-of-view of user  100 . 
       FIG. 4B  is a schematic illustration of the example of a second embodiment of apparatus  110 , from a side orientation of apparatus  110 . In addition to hanging ring  410 , as shown in  FIG. 4B , apparatus  110  may further include a clip  420 . User  100  can use clip  420  to attach apparatus  110  to a shirt or belt  150 , as illustrated in  FIG. 1C . Clip  420  may provide an easy mechanism for disengaging and re-engaging apparatus  110  from different articles of clothing. In other embodiments, apparatus  110  may include a female receptacle for connecting with a male latch of a car mount or universal stand. 
     In some embodiments, apparatus  110  includes a function button  430  for enabling user  100  to provide input to apparatus  110 . Function button  430  may accept different types of tactile input (e.g., a tap, a click, a double-click, a long press, a right-to-left slide, a left-to-right slide). In some embodiments, each type of input may be associated with a different action. For example, a tap may be associated with the function of taking a picture, while a right-to-left slide may be associated with the function of recording a video. 
     Apparatus  110  may be attached to an article of clothing (e.g., a shirt, a belt, pants, etc.), of user  100  at an edge of the clothing using a clip  431  as shown in  FIG. 4C . For example, the body of apparatus  100  may reside adjacent to the inside surface of the clothing with clip  431   engaging with the outside surface of the clothing. In such an embodiment, as shown in  FIG. 4C , the image sensor  220  (e.g., a camera for visible light) may be protruding beyond the edge of the clothing. Alternatively, clip  431  may be engaging with the inside surface of the clothing with the body of apparatus  110  being adjacent to the outside of the clothing. In various embodiments, the clothing may be positioned between clip  431  and the body of apparatus  110 . 
     An example embodiment of apparatus  110  is shown in  FIG. 4D . Apparatus  110  includes clip  431  which may include points (e.g.,  432 A and  432 B) in close proximity to a front surface  434  of a body  435  of apparatus  110 . In an example embodiment, the distance between points  432 A,  432 B and front surface  434  may be less than a typical thickness of a fabric of the clothing of user  100 . For example, the distance between points  432 A,  432 B and surface  434  may be less than a thickness of a tee-shirt, e.g., less than a millimeter, less than 2 millimeters, less than 3 millimeters, etc., or, in some cases, points  432 A,  432 B of clip  431  may touch surface  434 . In various embodiments, clip  431  may include a point  433  that does not touch surface  434 , allowing the clothing to be inserted between clip  431  and surface  434 . 
       FIG. 4D  shows schematically different views of apparatus  110  defined as a front view (F-view), a rearview (R-view), a top view (T-view), a side view (S-view) and a bottom view (B-view). These views will be referred to when describing apparatus  110  in subsequent figures.  FIG. 4D  shows an example embodiment where clip  431  is positioned at the same side of apparatus  110  as sensor  220  (e.g., the front side of apparatus  110 ). Alternatively, clip  431  may be positioned at an opposite side of apparatus  110  as sensor  220  (e.g., the rear side of apparatus  110 ). In various embodiments, apparatus  110  may include function button  430 , as shown in  FIG. 4D . 
     Various views of apparatus  110  are illustrated in  FIGS. 4E through 4K . For example,  FIG. 4F  show&#39;s a view of apparatus  110  with an electrical connection  441 . Electrical connection  441  may be, for example, a USB port, that may be used to transfer data to/from apparatus  110  and provide electrical power to apparatus  110 . In an example embodiment, connection  441  may be used to charge a battery  442  schematically shown in  FIG. 4E .  FIG. 4F  shows F-view of apparatus  110 , including sensor  220  and one or more microphones  443 . In some embodiments, apparatus  110  may include several microphones  443  facing outwards, wherein microphones  443  are configured to obtain environmental sounds and sounds of various speakers communicating with user  100 .  FIG. 4G  shows R-view of apparatus  110 . In some embodiments, microphone  444  may be positioned at the rear side of apparatus  110 , as shown in  FIG. 4G . Microphone  444  may be used to detect an audio signal from user  100 . It should be noted, that apparatus  110  may have microphones placed at any side (e.g., a front side, a rear side, a left side, a right side, a top side, or a bottom side) of apparatus  110 . In various embodiments,  some microphones may be at a first side (e.g., microphones  443  may be at the from of apparatus  110 ) and other microphones may be at a second side (e.g., microphone  444  may be at the back side of apparatus  110 ). 
       FIGS. 4H and 4I  show different sides of apparatus  110  (i.e., S-view of apparatus  110 ) consisted with disclosed embodiments. For example.  FIG. 4H  shows the location of sensor  220  and an example shape of clip  431 .  FIG. 4J  shows T-view of apparatus  110 , including function button  430 , and  FIG. 4K  shows B-view of apparatus  110  with electrical connection  441 . 
     The example embodiments discussed above with respect to  FIGS. 3A, 3B, 4A, and 4B  are not limiting. In some embodiments, apparatus  110  may be implemented in any suitable configuration for performing the disclosed methods. For example, referring back to  FIG. 2 , the disclosed embodiments may implement an apparatus  110  according to any configuration including an image sensor  220  and a processor unit  210  to perform image analysis and for communicating with a feedback unit  230 . 
       FIG. 5A  is a block diagram illustrating the components of apparatus  110  according to an example embodiment. As shown in  FIG. 5A , and as similarly discussed above, apparatus  110  includes an image sensor  220 , a memory  550 , a processor  210 , a feedback outputting unit  230 , a wireless transceiver  530 , and a mobile power source  520 . In other embodiments, apparatus  110  may also include buttons, other sensors such as a microphone, and inertial measurements devices such as accelerometers, gyroscopes, magnetometers, temperature sensors, color sensors, light sensors, etc. Apparatus  110  may further include a data port  570  and a power connection  510  with suitable interfaces for connecting with an external power source or an external device (not shown). 
     Processor  210 , depicted in  FIG. 5A , may include any suitable processing device. The term “processing device” includes any physical device having an electric circuit that performs a logic operation on input or inputs. For example, processing device may include one or more integrated circuits, microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field-programmable gate array (FPGA), or other circuits suitable for executing instructions or performing logic operations. The instructions executed by the processing device may, for example, be pre-loaded into a memory integrated with or embedded into the processing device or may be stored in a separate memory (e.g., memory  550 ). Memory  550  may comprise a Random Access Memory (RAM), a Read-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, or volatile memory, or any other mechanism capable of storing instructions.  
     Although, in the embodiment illustrated in  FIG. 5A , apparatus  110  includes one processing device (e.g., processor  210 ), apparatus  110  may include more than one processing device. Each processing device may have a similar construction, or the processing devices may be of differing constructions that are electrically connected or disconnected from each other. For example, the processing devices may be separate circuits or integrated in a single circuit. When more than one processing device is used, the processing devices may be configured to operate independently or collaboratively. The processing devices may be coupled electrically, magnetically, optically, acoustically, mechanically or by other means that permit them to interact. 
     In some embodiments, processor  210  may process a plurality of images captured from the environment of user  100  to determine different parameters related to capturing subsequent images. For example, processor  210  can determine, based on information derived from captured image data, a value for at least one of the following: an image resolution, a compression ratio, a cropping parameter, frame rate, a focus point, an exposure time, an aperture size, and a light sensitivity. The determined value may be used in capturing at least one subsequent image. Additionally, processor  210  can detect images including at least one hand-related trigger in the environment of the user and perform an action and/or provide an output of information to a user via feedback outputting unit  230 . 
     In another embodiment, processor  210  can change the aiming direction of image sensor  220 . For example, when apparatus  110  is attached with clip  420 , the aiming direction of image sensor  220  may not coincide with the field-of-view of user  100 . Processor  210  may recognize certain situations from the analyzed image data and adjust the aiming direction of image sensor  220  to capture relevant image data. For example, in one embodiment, processor  210  may detect an interaction with another individual and sense that the individual is not fully in view, because image sensor  220  is tilled down. Responsive thereto, processor  210  may adjust the aiming direction of image sensor  220  to capture image data of the individual. Other scenarios are also contemplated where processor  210  may recognize the need to adjust an aiming direction of image sensor  220 . 
     In some embodiments, processor  210  may communicate data to feedback-outputting unit  230 , which may include any device configured to provide information to a user  100 . Feedback outputting unit  230  may be provided as part of apparatus  110  (as shown) or may be provided external to apparatus  110  and communicatively coupled thereto. Feedback-outputting unit  230  may be configured to output visual or non visual feedback based on signals received from processor  210 , such as when processor  210  recognizes a hand-related trigger in the analyzed image data.  
     The term “feedback” refers to any output or information provided in response to processing at least one image in an environment. In some embodiments, as similarly described above, feedback may include an audible or visible indication of time information, detected text or numerals, the value of currency, a branded product, a person&#39;s identity, the identity of a landmark or other environmental situation or condition including the street names at an intersection or the color of a traffic light, etc., as well as other information associated with each of these. For example, in some embodiments, feedback may include additional information regarding the amount of currency still needed to complete a transaction, information regarding the identified person, historical information or times and prices of admission etc. of a detected landmark etc. In some embodiments, feedback may include an audible tone, a tactile response, and/or information previously recorded by user  100 , feedback-outputting unit  230  may comprise appropriate components for outputting acoustical and tactile feedback. For example, feedback-outputting unit  230  may comprise audio headphones, a hearing aid type device, a speaker, a bone conduction headphone, interfaces that provide tactile cues, vibrotactile stimulators, etc. In some embodiments, processor  210  may communicate signals with an external feedback outputting unit  230  via a wireless transceiver  530 , a wired connection, or some other communication interface. In some embodiments, feedback outputting unit  230  may also include any suitable display device for visually displaying information to user  100 . 
     As shown in  FIG. 5A , apparatus  110  includes memory  550 . Memory  550  may include one or more sets of instructions accessible to processor  210  to perform the disclosed methods, including instructions for recognizing a hand-related trigger in the image data. In some embodiments memory  550  may store image data (e.g., images, videos) captured from the environment of user  100 . In addition, memory  550  may store information specific to user  100 , such as image representations of known individuals, favorite products, personal items, and calendar or appointment information, etc. In some embodiments, processor  210  may determine, for example, which type of image data to store based on available storage space in memory  550 . In another embodiment, processor  210  may extract information from the image data stored in memory  550 . 
     As further shown in  FIG. 5A , apparatus  110  includes mobile power source  520 . The term “mobile power source” includes any device capable of providing electrical power, which can be easily carried by hand (e.g., mobile power source  520  may weigh less than a pound). The mobility of the power source enables user  100  to use apparatus  110  in a variety of situations. In some embodiments, mobile power source  520  may include one or more batteries (e.g., nickel-eadmium batteries, nickel-metal hydride batteries, and lithium-ion batteries) or any other type of electrical power supply. In other embodiments, mobile power source  520  may be  rechargeable and contained within a casing that holds apparatus  110 . In yet other embodiments, mobile power source  520  may include one or more energy harvesting devices for converting ambient energy into electrical energy (e.g., portable solar power units, human vibration units, etc.). 
     Mobile power source  520  may power one or more wireless transceivers (e.g., wireless transceiver  530  in  FIG. 5A ). The term “wireless transceiver” refers to any device configured to exchange transmissions over an air interface by use of radio frequency, infrared frequency, magnetic field, or electric field. Wireless transceiver  530  may use any known standard to transmit and/or receive data (e.g., Wi-Fi, Bluetooth®, Bluetooth Smart, 802.15.4, or ZigBee). In some embodiments, wireless transceiver  530  may transmit data (e.g., raw image data, processed image data, extracted information) from apparatus  110  to computing device  120  and/or server  250 . Wireless transceiver  530  may also receive data from computing device  120  and or server  250 . In other embodiments, wireless transceiver  530  may transmit data and instructions to an external feedback outputting unit  230 . 
       FIG. 5B  is a block diagram illustrating the components of apparatus  110  according to another example embodiment. In some embodiments, apparatus  110  includes a first image sensor  220   a , a second image sensor  220   b , a memory  550 , first processor  210   a , a second processor  210   b , a feedback outputting unit  230 , a wireless transceiver  530 , a mobile power source  520 , and a power connector  510 . In the arrangement shown in  FIG. 5B , each of the image sensors may provide images in a different image resolution, or face a different direction. Alternatively, each image sensor may be associated with a different camera (e.g., a wide angle camera, a narrow angle camera, an IR camera, etc.). In some embodiments, apparatus  110  can select which image sensor to use based on various factors. For example, processor  210   a  may determine, based on available storage space in memory  550 , to capture subsequent images in a certain resolution. 
     Apparatus  110  may operate in a first processing-mode and in a second processing-mode, such that the first processing-mode may consume less power than the second processing-mode. For example, in the first processing-mode, apparatus  110  may capture images and process the captured images to make real-time decisions based on an identifying hand-related trigger, for example. In the second processing-mode, apparatus  110  may extract information from stored images in memory  550  and delete images from memory  550 . In some embodiments, mobile power source  520  may provide more than fifteen hours of processing in the first processing-mode and about three hours of processing in the second processing-mode. Accordingly, different processing-modes may allow mobile power source  520  to produce  sufficient power for powering apparatus  110  for various time periods (e.g., more than two hours, more than four hours, more than ten hours, etc.). 
     In some embodiments, apparatus  110  may use first processor  210   a  in the first processing-mode when powered by mobile power source  520 , and second processor  210   b  in the second processing-mode when powered by external power source  580  that is connectable via power connector  510 . In other embodiments, apparatus  110  may determine, based on predefined conditions, which processors or which processing modes to use. Apparatus  110  may operate in the second processing-mode even when apparatus  110  is not powered by external power source  580 . For example, apparatus  110  may determine that it should operate in the second processing-mode when apparatus  110  is not powered by external power source  580 , if the available storage space in memory  550  for storing new image data is lower than a predefined threshold. 
     Although one wireless transceiver is depicted in  FIG. 5B , apparatus  110  may include more than one wireless transceiver (e.g., two wireless transceivers). In an arrangement with more than one wireless transceiver, each of the wireless transceivers may use a different standard to transmit and/or receive data. In some embodiments, a first wireless transceiver may communicate with server  250  or computing device  120  using a cellular standard (e.g., LTE or GSM), and a second wireless transceiver may communicate with server  250  or computing device  120  using a short-range standard (e.g., Wi-Fi or Bluetooth®). In some embodiments, apparatus  110  may use the first wireless transceiver when the wearable apparatus is powered by u mobile power source included in the wearable apparatus, and use the second wireless transceiver when the wearable apparatus is powered by an external power source. 
       FIG. 5C  is a block diagram illustrating the components of apparatus  110  according to another example embodiment including computing device  120 . In this embodiment, apparatus  110  includes an image sensor  220 , a memory  550   a , a first processor  210 , a feedback-outputting unit  230 , a wireless transceiver  530   a , a mobile power source  520 , and a power connector  510 . As further shown in  FIG. 5C , computing device  120  includes a processor  540 , a feedback-outputting unit  545 , a memory  550   b , a wireless transceiver  530   b , and a display  260 . One example of computing device  120  is a smartphone or tablet having a dedicated application installed therein. In other embodiments, computing device  120  may include any configuration such as an on-board automobile computing system, a PC, a laptop, and any other system consistent with the disclosed embodiments. In this example, user  100  may view feedback output in response to identification of a hand-related trigger tin display  260 . Additionally, user  100  may view other data (e.g., images, video clips, object information, schedule information, extracted information, etc.) on display  260 . In addition, user  100  may communicate with server  250  via computing device  120 .  
     In some embodiments, processor  210  and processor  540  are configured Co extract information from captured image data. The term “extracting information” includes any process by which information associated with objects, individuals, locations, events, etc., is identified in the captured image data by airy means known to those of ordinary skill in the art. In some embodiments, apparatus  110  may use the extracted information to send feedback or other real-time indications to feedback outputting unit  230  or to computing device  120 . In some embodiments, processor  210  may identify in the image data the individual standing in front of user  100 , and send computing device  120  the name of the individual and the last time user  100  met the individual. In another embodiment, processor  210  may identify in the image data, one or more visible triggers, including a hand-related trigger, and determine whether the trigger is associated with a person other than the user of the wearable apparatus to selectively determine whether to perform an action associated with the trigger. One such action may be to provide a feedback to user  100  via feedback-outputting unit  230  provided as part of (or in communication with) apparatus  110  or via a feedback unit  545  provided as part of computing dev ice  120 . For example, feedback-outputting unit  545  may be in communication with display  260  to cause the display  260  to visibly output information. In some embodiments, processor  210  may identify in the image data a hand-related trigger and send computing device  120  an indication of the trigger. Processor  540  may then process the received trigger information and provide an output via feedback outputting unit  545  or display  260  based on the hand-related trigger. In other embodiments, processor  540  may determine a hand-related trigger and provide suitable feedback similar to the above, based on image data received from apparatus  110 . In some embodiments, processor  540  may provide instructions or other information, such as environmental information to apparatus  110  based on an identified hand-related trigger. 
     In some embodiments, processor  210  may identify other environmental information in the analyzed images, such as an individual standing in front user  100 , and send computing device  120  information related to the analyzed information such as the name of the individual and the last time user  100  met the individual. In a different embodiment, processor  540  may extract statistical information from captured image data and forward the statistical information to server  250 . For example, certain information regarding the types of items a user purchases, or the frequency a user patronizes a particular merchant, etc. may be determined by processor  540 . Based on this information, server  250  may send computing device  120  coupons and discounts associated with the user&#39;s preferences. 
     When apparatus  110  is connected or wirelessly connected to computing device  120 , apparatus  110  may Transmit at least part of the image data stored in memory  550   a  for storage in memory  550   b . In some embodiments, after computing device  120  confirms that  transferring the part of image data was successful, processor  540  may delete the part of the image data. The term “delete” means that the image is marked as “deleted” and other image data may be stored instead of it, but does not necessarily mean that the image data was physically removed from the memory. 
     As will be appreciated by a person skilled in the art having the benefit of this disclosure, numerous variations and/or modifications may be made to the disclosed embodiments. Not all components are essential for the operation of apparatus  110 . Any component may be located in any appropriate apparatus and the components may be rearranged into a variety of configurations while providing the functionality of the disclosed embodiments. For example, in some embodiments, apparatus  110  may include a camera, a processor, and a wireless transceiver for sending data to another device. Therefore, the foregoing configurations are examples and, regardless of the configurations discussed above, apparatus  110  can capture, store, and/or process images. 
     Further, the foregoing and following description refers to storing and or processing images or image data. In the embodiments disclosed herein, the stored and/or processed images or image data may comprise a representation of one or more images captured by image sensor  220 . As the term is used herein, a “representation” of an image (or image data) may include an entire image or a portion of an image. A representation of an image (or image data) may have the same resolution or a lower resolution as the image (or image data), and/or a representation of an image (or image data) may be altered in some respect (e.g., be compressed, have a lower resolution, have one or more colors that are altered, etc.). 
     For example, apparatus  110  may capture an image and store a representation of the image that is compressed as a JPG file. As another example, apparatus  110  may capture an image in color, but store a black-and-white representation of the color image. As yet another example, apparatus  110  may capture an image and store a different representation of the image (e.g., a portion of the image). For example, apparatus  110  may store a portion of an image that includes a face of a person who appears in the image, but that does not substantially include the environment surrounding the person. Similarly, apparatus  110  may, for example, store a portion of an image that includes a product that appears in the image, but does not substantially include the environment surrounding the product. As yet another example, apparatus  110  may store a representation of an image at a reduced resolution (i.e., at a resolution that is of a lower value than that of the captured image). Storing representations of images may allow apparatus  110  to save storage space in memory  550 . Furthermore, processing representations of images may allow apparatus  110  to improve processing efficiency and/or help to preserve battery life.  
     In addition to the above, in some embodiments, any one of apparatus  110  or computing device  120 , via processor  210  or  540 , may further process the captured image data to provide additional functionality to recognize objects and/or gestures and/or other information in the captured image data. In same embodiments, actions may be taken based on the identified objects, gestures, or other information. In some embodiments, processor  210  or  540  may identify in the image data, one or more visible triggers, including a hand-related trigger, and determine whether the trigger is associated with a person other than the user to determine whether to perform an action associated with the trigger. 
     Some embodiments of the present disclosure may include an apparatus securable to an article of clothing of a user. Such an apparatus may include two portions, connectable by a connector. A capturing unit may be designed to be worn on the outside of a user&#39;s clothing, and may include an image sensor for capturing images of a user&#39;s environment. The capturing unit may be connected to or connectable to a power unit, which may be configured to house a power source and a processing device. The capturing unit may be a small device including a camera or other device for capturing images. The capturing unit may be designed to be inconspicuous and unobtrusive, and may be configured to communicate with a power unit concealed by a user&#39;s clothing. The power unit may include bulkier aspects of the system, such as transceiver antennas, at least one battery, a processing device, etc. In some embodiments, communication between the capturing unit and the power unit may be provided by a data cable included in the connector, while in other embodiments, communication may be wirelessly achieved between the capturing unit and the power unit. Some embodiments may permit alteration of the orientation of an image sensor of the capture unit, for example to better capture images of interest. 
       FIG. 6  illustrates an exemplary embodiment of a memory containing soft ware modules consistent with the present disclosure. Included in memory  550  are orientation identification module  601 , orientation adjustment module  602 , and motion tracking module  603 . Modules  601 ,  602 ,  603  may contain software instructions for execution by at least one processing device, e.g., processor  210 , included with a wearable apparatus. Orientation identification module  601 , orientation adjustment module  602 , and motion tracking module  603  may cooperate to provide orientation adjustment for a capturing unit incorporated into wireless apparatus  110 . 
       FIG. 7  illustrates an exemplary capturing unit  710  including an orientation adjustment unit  705 . Orientation adjustment unit  705  may be configured to permit the adjustment of image sensor  220 . As illustrated in  FIG. 7 , orientation adjustment unit  705  may include an eye-ball type adjustment mechanism. In alternative embodiments, orientation  adjustment unit  705  may include gimbals, adjustable stalks, pivotable mounts, and any other suitable unit for adjusting an orientation of image sensor  220 . 
     Image sensor  220  may be configured to be movable with the head of user  100  in such a manner that an aiming direction of image sensor  220  substantially coincides with a field of view of user  100 . For example, as described above, a camera associated with image sensor  220  may be installed within capturing unit  710  at a predetermined angle in a position facing slightly upwards or downwards, depending on an intended location of capturing unit  710 . Accordingly, the set aiming direction of image sensor  220  may match the field-of-view of user  100 . In some embodiments, processor  210  may change the orientation of image sensor  220  using image data provided from image sensor  220 . For example, processor  210  may recognize that a user is reading a book and determine that the aiming direction of image sensor  220  is offset from the text. That is, because the words in the beginning of each line of text are not fully in view, processor  210  may determine that image sensor  220  is tilted in the wrong direction. Responsive thereto, processor  210  may adjust the aiming direction of image sensor  220 . 
     Orientation identification module  601  may be configured to identify an orientation of an image sensor  220  of capturing unit  710 . An orientation of an image sensor  220  may be identified, for example, by analysis of images captured by image sensor  220  of capturing unit  710 , by tilt or attitude sensing devices within capturing unit  710 , und by measuring a relative direction of orientation adjustment unit  705  with respect to the remainder of capturing unit  710 . 
     Orientation adjustment module  602  may be configured to adjust an orientation of image sensor  220  of capturing unit  710 . As discussed above, image sensor  220  may be mounted on an orientation adjustment unit  705  configured for movement. Orientation adjustment unit  705  may be configured for rotational and/or lateral movement in response to commands from orientation adjustment module  602 . In some embodiments orientation adjustment unit  705  may be adjust an orientation of image sensor  220  via motors, electromagnets, permanent magnets, and/or any suitable combination thereof. 
     In some embodiments, monitoring module  603  may be provided for continuous monitoring. Such continuous monitoring may include tracking a movement of at least a portion of an object included in one or more images captured by the image sensor. For example, in one embodiment, apparatus  110  may track an object as long as the object remains substantially within the field-of-view of image sensor  220 . In additional embodiments, monitoring module  603  may engage orientation adjustment module  602  to instruct orientation adjustment unit  705  to continually orient image sensor  220  towards an object of interest. For example, in one embodiment, monitoring module  603  may cause image sensor  220  to adjust an orientation to  ensure that a certain designated object, for example, the face of a particular person, remains within the field-of view of image sensor  220 , even as that designated object moves about. In another embodiment, monitoring module  603  may continuously monitor an area of interest included in one or more images captured by the image sensor. For example, a user may be occupied by a certain task, for example, typing on a laptop, while image sensor  220  remains oriented in a particular direction and continuously monitors a portion of each image from a series of images to detect a trigger or other event. For example, image sensor  210  may be oriented towards a piece of laboratory equipment and monitoring module  603  may be configured to monitor a status light on the laboratory equipment for a change in status, while the user&#39;s attention is otherwise occupied. 
     In some embodiments consistent with the present disclosure, capturing unit  710  may include a plurality of image sensors  220 . The plurality of image sensors  220  may each be configured to capture different image data. For example, when a plurality of image sensors  220  are provided, the image sensors  220  may capture images having different resolutions, may capture wider or narrower fields of view, and may have different levels of magnification. Image sensors  220  may be provided with varying lenses to permit these different configurations. In some embodiments, a plurality of image sensors  220  may include image sensors  220  having different orientations. Thus, each of the plurality of image sensors  220  may be pointed in a different direction to capture different images. The fields of view of image sensors  220  may be overlapping in some embodiments. The plurality of image sensors  220  may each be configured for orientation adjustment, for example, by being paired with an image adjustment unit  705 . In some embodiments, monitoring module  603 , or another module associated with memory  550 , may be configured to individually adjust the orientations of the plurality of image sensors  220  as well as to turn each of the plurality of image sensors  220  on or off as may be required. In some embodiments, monitoring an object or person captured by an image sensor  220  may include tracking movement of the object across the fields of view of the plurality of image sensors  220 . 
     Embodiments consistent with the present disclosure may include connectors configured to connect a capturing unit and a power unit of a wearable apparatus. Capturing units consistent with the present disclosure may include least one image sensor configured to capture images of an environment of a user. Power units consistent with the present disclosure may be configured to house a power source and/or at least one processing device. Connectors consistent with the present disclosure may be configured to connect the capturing unit and the power unit, and may be configured to secure the apparatus to an article of clothing such that the capturing unit is positioned over an outer surface of the article of clothing and the power unit is positioned under an inner surface of the article of clothing. Exemplary embodiments of capturing units,  connectors, and power units consistent with the disclosure are discussed in further detail with respect to  FIGS. 8-14 . 
       FIG. 8  is a schematic illustration of an embodiment of wearable apparatus  110  securable to an article of clothing consistent with the present disclosure. As illustrated in  FIG. 8 . capturing unit  710  and power unit  720  may be connected by a connector  730  such that capturing unit  710  is positioned on one side of an article of clothing  750  and power unit  720  is positioned on the opposite side of the clothing  750 . In some embodiments, capturing unit  710  may be positioned over an outer surface of the article of clothing  750  and power unit  720  may be located under an inner surface of the article of clothing  750 . The power unit  720  may be configured to be placed against the skin of a user. 
     Capturing unit  710  may include an image sensor  220  and an orientation adjustment unit  705  (as illustrated in  FIG. 7 ). Power unit  720  may include mobile power source  520  and processor  210 . Power unit  720  may further include any combination of elements previously discussed that may be a part of wearable apparatus  110 , including, but not limited to, wireless transceiver  530 , feedback outputting unit  230 , memory  550 , and data port  570 . 
     Connector  730  may include a clip  715  or other mechanical connection designed to clip or attach capturing unit  710  and power unit  720  to an article of clothing  750  as illustrated in  FIG. 8 . As illustrated, clip  715  may connect to each of capturing unit  710  and power unit  720  at a perimeter thereof, and may wrap around an edge of the article of clothing  750  to affix the capturing unit  710  and power unit  720  in place. Connector  730  may further include a power cable  760  and a data cable  770 . Power cable  760  may be capable of conveying power from mobile power source  520  to image sensor  220  of capturing unit  710 . Power cable  760  may also be configured to provide power to any other elements of capturing unit  710 , e.g., orientation adjustment unit  705 . Data cable  770  may be capable of conveying captured image data from image sensor  220  in capturing unit  710  to processor  800  in the power unit  720 . Data cable  770  may be further capable of conveying additional data between capturing unit  710  and processor  800 , e.g., control instructions for orientation adjustment unit  705 . 
       FIG. 9  is a schematic illustration of a user  100  wearing a wearable apparatus  110  consistent with an embodiment of the present disclosure. As illustrated in  FIG. 9 , capturing unit  710  is located on an exterior surface of the clothing  750  of user  100 . Capturing unit  710  is connected to power unit  720  (not seen in this illustration) via connector  730 , which wraps around an edge of clothing  750 . 
     In some embodiments, connector  730  may include u flexible printed circuit board (PCB).  FIG. 10  illustrates an exemplary embodiment wherein connector  730  includes a flexible printed circuit board  765 . Flexible printed circuit board  765  may include data connections and  power connections between capturing unit  710  and power unit  720 . Thus, in some embodiments, flexible printed circuit board  765  may serve to replace power cable  760  and data cable  770 . In alternative embodiments, flexible printed circuit board  765  may be included in addition to at least one of power cable  760  and data cable  770 . In various embodiments discussed herein, flexible printed circuit board  765  may be substituted for, or included in addition to, power cable  760  and data cable  770 . 
       FIG. 11  is a schematic illustration of another embodiment of a wearable apparatus securable to an article of clothing consistent with the present disclosure. As illustrated in  FIG. 11 , connector  730  may be centrally located with respect to capturing unit  710  and power unit  720 . Central location of connector  730  may facilitate affixing apparatus  110  to clothing  750  through a hole in clothing  750  such as, for example, a button-hole in an existing article of clothing  750  or a specialty hole in an article of clothing  750  designed to accommodate wearable apparatus  110 . 
       FIG. 12  is a schematic illustration of still another embodiment of wearable apparatus  110  securable to an article of clothing. As illustrated in  FIG. 12 , connector  730  may include a first magnet  731  and a second magnet  732 . First magnet  731  and second magnet  732  may secure capturing unit  710  to power unit  720  with the article of clothing positioned between first magnet  731  and second magnet  732 . In embodiments including first magnet  731  and second magnet  732 , power cable  760  and data cable  770  may also be included. In these embodiments, power cable  760  and data cable  770  may be of any length, and may provide a flexible power and data connection between capturing unit  710  and power unit  720 . Embodiments including first magnet  731  and second magnet  732  may further include a flexible PCB  765  connection in addition to or instead of power cable  760  and/or data cable  770 . In some embodiments, first magnet  731  or second magnet  732  may be replaced by an object comprising a metal material. 
       FIG. 13  is a schematic illustration of yet another embodiment of a wearable apparatus  110  securable to an article of clothing.  FIG. 13  illustrates an embodiment wherein power and data may be wirelessly transferred between capturing unit  710  and power unit  720 . As illustrated in  FIG. 13 , first magnet  731  and second magnet  732  may be provided as connector  730  to secure capturing unit  710  and power unit  720  to an article of clothing  750 . Power anchor data may be transferred between capturing unit  710  and power unit  720  via any suitable wireless technology, for example, magnetic and/or capacitive coupling, near field communication technologies, radiofrequency transfer, and any other wireless technology suitable for transferring data and/or power across short distances.  
       FIG. 14  illustrates still another embodiment of wearable apparatus  110  securable to an article of clothing  750  of a user. As illustrated in  FIG. 14 , connector  730  may include features designed for a contact fit. For example, capturing unit  710  may include a ring  733  with a hollow center having a diameter slightly larger than a disk-shaped protrusion  734  located on power unit  720 . When pressed together with fabric of an article of clothing  750  between them, disk-shaped protrusion  734  may fit lightly inside ring  733 , securing capturing unit  710  to power unit  720 .  FIG. 14  illustrates an embodiment that does not include any cabling or other physical connection between capturing unit  710  and power unit  720 . In this embodiment, capturing unit  710  and power unit  720  may transfer power and data wirelessly. In alternative embodiments, capturing unit  710  and power unit  720  may transfer power and data via at least one of cable  760 , data cable  770 , and flexible printed circuit board  765 . 
       FIG. 15  illustrates another aspect of power unit  720  consistent with embodiments described herein. Power unit  720  may be configured to be positioned directly against the user&#39;s skin. To facilitate such positioning, power unit  720  may further include at least one surface coated with a biocompatible material  740 . Biocompatible materials  740  may include materials that will not negatively react with the skin of the user when worn against the skin for extended periods of time. Such materials may include, for example, silicone, PTFE, kaplon, polyimide, titanium, nitinol, platinum, and others. Also as illustrated in  FIG. 15 , power unit  720  may be sized such that an inner volume of the power unit is substantially filled by mobile power source  520 . That is, in some embodiments, the inner volume of power unit  720  may be such that the volume does not accommodate any additional components except for mobile power source  520 . In some embodiments, mobile power source  520  may take advantage of its close proximity to the skin of user&#39;s skin. For example, mobile power source  520  may use the Peltier effect to produce power and/or charge the power source. 
     In further embodiments, an apparatus securable to an article of clothing may further include protective circuitry associated with power source  520  housed in in power unit  720 .  FIG. 16  illustrates an exemplary embodiment including protective circuitry  775 . As illustrated in  FIG. 16 , protective circuitry  775  may be located remotely with respect to power unit  720 . In alternative embodiments, protective circuitry  775  may also be located in capturing unit  710 . on flexible printed circuit board  765 , or in power unit  720 . 
     Protective circuitry  775  may be configured to protect image sensor  220  and/or other elements of capturing unit  710  from potentially dangerous currents and/or voltages produced by mobile power source  520 . Protective circuitry  775  may include passive components such as capacitors, resistors, diodes, inductors, etc., to provide protection to elements of capturing unit  710 . In some embodiments, protective circuitry  775  may also include  active components, such as transistors, to provide protection to elements of capturing unit  710 . For example, in some embodiments, protective circuitry  775  may comprise one or more resistors serving as fuses. Each fuse may comprise a wire or strip that melts (thereby braking a connection between circuitry of image capturing unit  710  and circuitry of power unit  720 ) when current flowing through the fuse exceeds a predetermined limit (e.g., 500 milliamps, 900 milliamps, 1 amp, 1.1 amps, 2 amp, 2.1 amps, 3 amps, etc.) Any or all of the previously described embodiments may incorporate protective circuitry  775 . 
     In some embodiments, the wearable apparatus may transmit data to a computing device (e.g., a smartphone, tablet, watch, computer, etc.) over one or more networks via any known wireless standard (e.g., cellular, Wi-Fi, Bluetooth®, etc.), or via near-filed capacitive coupling, other short range wireless techniques, or via a wired connection. Similarly, the wearable apparatus may receive data from the computing device over one or more networks via any known wireless standard (e.g., cellular, Wi-Fi, bluetooth®, etc.), or via near-filed capacitive coupling, other short range wireless techniques, or via a wired connection. The data transmitted to the wearable apparatus and/or received by the wireless apparatus may include images, portions of images, identifiers related to information appearing in analyzed images or associated with analyzed audio, or any other data representing image and/or audio data. For example, an image may be analyzed and an identifier related to an activity occurring in the image may be transmitted to the computing device (e.g., the “paired device”). In the embodiments described herein, the wearable apparatus may process images and/or audio locally (on board the wearable apparatus) and/or remotely (via a computing device). Further, in the embodiments described herein, the wearable apparatus may transmit data related to the analysis of images and/or audio to a computing device for further analysis, display, and/or transmission to another device (e.g., a paired device). Further, a paired device may execute one or more applications (apps) to process, display, and/or analyze data (e.g., identifiers, text, images, audio, etc.) received from the wearable apparatus. 
     Some of the disclosed embodiments may involve systems, devices, methods, and software products for determining at least one keyword. For example, at least one keyword may be determined based on data collected by apparatus  110 . At least one search query may be determined based on the at least one keyword. The at least one search query may be transmitted to a search engine. 
     In some embodiments, at least one keyword may be determined based on at least one or more images captured by image sensor  220 . In some cases, the at least one keyword may be selected from a keywords pool stored in memory. In some cases, optical character recognition (OCR) may be performed on at least one image captured by image sensor  220 , and the at least  one keyword may be determined based on the OCR result. In some cases, at least one image captured by image sensor  220  may be analyzed to recognize: a person, an object, a location, a scene, and so forth. Further, the at least one keyword may be determined based on the recognized person, object, location, scene, etc. For example, the at least one keyword may comprise: a person&#39;s name, an object&#39;s name, a place&#39;s name, a date, a sport team&#39;s name, a movie&#39;s name, a book&#39;s name, and so forth. 
     In some embodiments, at least one keyword may be determined based on the user&#39;s behavior. The user&#39;s behavior may be determined based on an analysis of the one or more images captured by image sensor  220 . In some embodiments, at least one keyword may be determined based on activities of a user and/or other person. The one or more images captured by image sensor  220  may be analyzed to identify the activities of the user and/or the other person who appears in one or more images captured by image sensor  220 . In some embodiments, at least one keyword may be determined based on at least one or more audio segments captured by apparatus  110 . In some embodiments, at least one keyword may be determined based on at least GPS information associated with the user. In some embodiments, at least one keyword may be determined based on at least the current time and/or date. 
     In some embodiments, at least one search query may be determined based on at least one keyword. In some cases, the at least one search query may comprise the at least one keyword. In some cases, the at least one search query may comprise the at least one keyword and additional keywords provided by the user. In some cases, the at least one search query may comprise the at least one keyword and one or more images, such as images captured by image sensor  220 . In some cases, the at least one search query may comprise the at least one keyword and one or more audio segments, such as audio segments captured by apparatus  110 . 
     In some embodiments, the at least one search query may be transmitted to a search engine. In some embodiments, search results provided by the search engine in response to the at least one search query may be provided to tin; user. In some embodiments, the at least one search query may be used to access a database. 
     For example, in one embodiment, the keywords may include a name of a type of food, such as quinoa, or a brand name of a food product: and the search will output information related to desirable quantities of consumption, facts about the nutritional profile, and so forth. In another example, in one embodiment, the keywords may include a name of a restaurant, and the search will output information related to tire restaurant, such as a menu, opening hours, reviews, and so forth. The name of the restaurant may be obtained using OCR on an image of signage, using GPS information, and so forth. In another example, in one embodiment, the keywords may include a name of a person, and the search wall provide information from a social network  profile of the person. The name of the parson may be obtained using OCR on an image of a name lag attached to the person&#39;s shirt, using face recognition algorithms, and so forth. In another example, in one embodiment, the keywords may include a name of a book, and the search will output information related to the book, such as reviews, sales statistics, information regarding the author of the book, and so forth. In another example, in one embodiment, the keywords may include a name of a movie, and the search will output information related to the movie, such as reviews, box office statistics, information regarding the cast of the movie, show limes, and so forth. In another example, in one embodiment, the keywords may include a name of a sport team, and the search will output information related to the sport team, such as statistics, latest results, future schedule, information regarding the players of the sport team, and so forth. For example, the name of the sport team may be obtained using audio recognition algorithms. 
     Camera-Based Directional Hearing Aid 
     As discussed previously, the disclosed embodiments may include providing feedback, such as acoustical and tactile feedback, to one or more auxiliary devices in response to processing at least one image in an environment. In some embodiments, the auxiliary device may be an earpiece or other device used to provide auditory feedback to the user, such as a hearing aid. Traditional hearing aids often use microphones to amplify sounds in the user&#39;s environment. These traditional systems, however, are often unable to distinguish between sounds that may be of particular importance to the wearer of the device, or may do so on a limited basis. Using the systems and methods of the disclosed embodiments, various improvements to traditional hearing aids are provided, as described in detail below. 
     In one embodiment, a camera-based directional hearing aid may be provided for selectively amplifying sounds based on a look direction of a user. The hearing aid may communicate with an image capturing device, such as apparatus  110 , to determine the look direction of the user. This look direction may be used to isolate and/or selectively amplify sounds received from that direction (e.g., sounds from individuals in the user&#39;s look direction, etc.). Sounds received from directions other than the user&#39;s look direction may be suppressed, attenuated, filtered or the like. 
       FIG. 17A  is a schematic illustration of an example of a user  100  wearing an apparatus  110  for a camera-based hearing interface device  1710  according to a disclosed embodiment. User  100  may wear apparatus  110  that is physically connected to a shirt or other piece of clothing of user  100 , as shown. Consistent with the disclosed embodiments, apparatus  110  may be positioned in other locations, as described previously. For example, apparatus  110  may be physically connected to a necklace, a belt, glasses, a wrist strap, a button, etc. Apparatus   110  may be configured to communicate with a hearing interface device such as hearing interface device  1710 . Such communication may be through a wired connection, or may be made wirelessly (e.g., using a Bluetooth™, NFC, or forms of wireless communication). In some embodiments, one or more additional devices may also be included, such as computing device  120 . Accordingly, one or more of the processes or functions described herein with respect to apparatus  110  or processor  210  may be performed by computing device  120  and/or processor  540 . 
     Hearing interface device  1710  may be any device configured to provide audible feedback to user  100 . Hearing interlace device  1710  may correspond to feedback outputting unit  230 , described above, and therefore any descriptions of feedback outputting unit  230  may also apply to hearing interface device  1710 . In some embodiments, hearing interface device  1710  may be separate from feedback outputting unit  230  and may be configured to receive signals from feedback outputting unit  230 . As shown in  FIG. 17A , hearing interface device  1710  may be placed in one or both cars of user  100 , similar to traditional hearing interface devices. Hearing interface device  1710  may be of various styles, including in-the-canal, completely-in-canal, in-the-ear, behind-the-ear, on-the-ear, receiver-in-canal, open fit, or various other styles. Hearing interface device  1710  may include one or mote speakers for providing audible feedback to user  100 , microphones for detecting sounds in the environment of user  100 , internal electronics, processors, memories, etc. In some embodiments, in addition to or instead of a microphone, hearing interface device  1710  may comprise one or more communication units, and in particular one or more receivers for receiving signals from apparatus  110  and transferring the signals to user  100 . 
     Hearing interface device  1710  may have various other configurations or placement locations. In some embodiments, hearing interface device  1710  may comprise a bone conduction headphone  1711 , as shown in  FIG. 17A . Bone conduction headphone  1711  may be surgically implanted and may provide audible feedback to user  100  through bone conduction of sound vibrations to the inner ear. Hearing interface device  1710  may also comprise one or more headphones (e.g., wireless headphones, over-ear headphones, etc.) or a portable speaker carried or worn by user  100 . In some embodiments, hearing interface device  1710  may be integrated into other devices, such as a Bluetooth™ headset of the user, glasses, a helmet (e.g., motorcycle helmets, bicycle helmets, etc.), a hat, etc. 
     Apparatus  110  may be configured to determine a user look direction  1750  of user  100 . In some embodiments, user look direction  1750  may be tracked by monitoring a direction of the chin, or another body pan or face part of user  100  relative to an optical axis of a camera sensor  1751 . Apparatus  110  may be configured to capture one or more images of the  surrounding environment of user, for example, using image sensor  220 . The captured images may include a representation of a chin of user  100 , which may be used to determine user look direction  1750 . Processor  210  (and/or processors  210   a  and  210   b ) may be configured to analyze the captured images and detect the chin or another part of user  100  using various image detection or processing algorithms (e.g., using convolutional neural networks (CNN), scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) features, or other techniques). Based on the detected representation of a chin of user  100 , look direction  1750  may be determined. Look direction  1750  may be determined in part by comparing the detected representation of a chin of user  100  to an optical axis of a camera sensor  1751 . For example, the optical axis  1751  may be known or fixed in each image and processor  210  may determine look direction  1750  by comparing a representative angle of the chin of user  100  to the direction of optical axis  1751 . While the process is described using a representation of a chin of user  100 , various other features may be detected for determining user look direction  1750 , including the user&#39;s face, nose, eyes, hand, etc. 
     In other embodiments, user look direction  1750  may be aligned more closely with the optical axis  1751 . For example, as discussed above, apparatus  110  may be affixed to a pair of glasses of user  100 , as shown in  FIG. 1A . In this embodiment, user look direction  1750  may be the same as or close to the direction of optical axis  1751 . Accordingly, user look direction  1750  may be determined or approximated based on the view of image sensor  220 . 
       FIG. 17B  is a schematic illustration of an embodiment of an apparatus securable to an article of clothing consistent with the present disclosure. Apparatus  110  may be securable to a piece of clothing, such as the shirt of user  110 , as shown in  FIG. 17A . Apparatus  110  may be securable to other articles of clothing, such as a belt or pants of user  100 , as discussed above. Apparatus  110  may have one or more cameras  1730 , which may correspond to image sensor  220 . Camera  1730  may be configured to capture images of the surrounding environment of user  100 . In some embodiments, camera  1730  may be configured to detect a representation of a chin of the user in the same images capturing the surrounding environment of the user, which may be used for other functions described in this disclosure. In other embodiments camera  1730  may be an auxiliary or separate camera dedicated to determining user look direction  1750 . 
     Apparatus  110  may further comprise one or more microphones  1720  for capturing sounds from the environment of user  100 . Microphone  1720  may also be configured to determine a directionality of sounds in the environment of user  100 . For example, microphone  1720  may comprise one or more directional microphones, which may be more sensitive to picking up sounds in certain directions. For example, microphone  1720  may comprise a unidirectional microphone, designed to pick up sound from a single direction or small range of  directions. Microphone  1720  may also comprise a cardioid microphone, which may be sensitive to sounds from the front and sides. Microphone  1720  may also include a microphone array, which may comprise additional microphones, such as microphone  1721  on the front of apparatus  110 , or microphone  1722 , placed on the side of apparatus  110 . In some embodiments, microphone  1720  may be a multi-port microphone for capturing multiple audio signals. The microphones shown in  FIG. 17B  are by way of example only, and any suitable number, configuration, or location of microphones may be utilized. Processor  210  may be configured to distinguish sounds within the environment of user  100  and determine an approximate directionality of each sound. For example, using an array of microphones  1720 , processor  210  may compare the relative timing or amplitude of an individual sound among the microphones  1720  to determine a directionality relative to apparatus  100 . 
     As a preliminary step before other audio analysis operations, the sound captured from an environment of a user may be classified using any audio classification technique. For example, the sound may be classified into segments containing music, tones, laughter, screams, or the like. Indications of the respective segments may be logged in a database and may prove highly useful for life logging applications. As one example, the logged information may enable the system to retrieve and/or determine a mood when the user met another person. Additionally, such processing is relatively fast and efficient, and does not require significant computing resources, and transmitting the information to a destination does not require significant bandwidth. Moreover, once certain parts of the audio are classified as non-speech, more computing resources may be available for processing the other segments. 
     Based on the determined user look direction  1750 , processor  210  may selectively condition or amplify sounds from a region associated with user look direction  1750 .  FIG. 18  is a schematic illustration showing an exemplary environment for use of a camera-based hearing aid consistent with the present disclosure. Microphone  1720  may detect one or more sounds  1820 ,  1821 , and  1822  within the environment of user  100 . Based on user look direction  1750 , determined by processor  210 , a region  1830  associated with user look direction  1750  may be determined. As shown in  FIG. 18 , region  1830  may be defined by a cone or range of directions based on user look direction  1750 . The range of angles may be defined by an angle, θ, as shown in  FIG. 18 . The angle, θ, may be any suitable angle for defining a range for conditioning sounds within the environment of user  100  (e.g., 10 degrees, 20 degrees, 45 degrees). 
     Processor  210  may be configured to cause selective conditioning of sounds in the environment of user  100  based on region  1830 . The conditioned audio signal may be transmitted to hearing interface device  1710 , and thus may provide user  100  with audible feedback corresponding to the look direction of the user. For example, processor  210  may  determine that sound  1820  (which may correspond to the voice of an individual  1810 , or to noise for example) is within region  1830 . Processor  210  may then perform various conditioning techniques on the audio signals received from microphone  1720 . The conditioning may include amplifying audio signals determined to correspond to sound  1820  relative to other audio signals. Amplification may be accomplished digitally, for example by processing audio signals associated with  1820  relative to other signals. Amplification may also be accomplished by-changing one or more parameters of microphone  1720  to focus on audio sounds emanating from region  1830  (e.g., a region of interest) associated with user look direction  1750 . For example, microphone  1720  may be a directional microphone that and processor  210  may perform an operation to focus microphone  1720  on sound  1820  or other sounds within region  1830 . Various other techniques for amplifying sound  1820  may be used, such as using a beam forming microphone array, acoustic telescope techniques, etc. 
     Conditioning may also include attenuation or suppressing one or more audio signals received from directions outside of region  1830 . For example, processor  1820  may attenuate sounds  1821  and  1822 . Similar to amplification of sound  1820 , attenuation of sounds may occur through processing audio signals, or by varying one or more parameters associated with one or more microphones  1720  to direct focus away from sounds emanating from outside of region  1830 . 
     In some embodiments, conditioning may further include changing a tone of audio signals corresponding to sound  1820  to make sound  1820  more perceptible to user  100 . For example, user  100  may have lesser sensitivity to tones in a certain range and conditioning of the audio signals may adjust the pitch of sound  1820  to make it more perceptible to user  100 . For example, user  100  may experience hearing loss in frequencies above 10 khz. Accordingly, processor  210  may remap higher frequencies (e.g.. at 15 khz) to 10 khz. In some embodiments processor  210  may be configured to change a rate of speech associated with one or more audio signals. Accordingly, processor  210  may be configured to detect speech within one or more audio signals received by microphone  1720 , for example using voice activity detection (VAD) algorithms or techniques. If sound  1820  is determined to correspond to voice or speech, for example from individual  1810 , processor  220  may be configured to vary the playback rate of sound  1820 . For example, the rate of speech of individual  1810  may be decreased to make the detected speech more perceptible to user  100 . Various other processing may be performed, such as modifying the tone of sound  1820  to maintain the same pitch as the original audio signal, or to reduce noise within the audio signal. If speech recognition has been performed on the audio signal associated with sound  1820 , conditioning may further include modifying the audio signal based on the detected speech. For example, processor  210  may introduce pauses or increase the  duration of pauses between words and/or sentences, which may make the speech easier to understand. 
     The conditioned audio signal may then be transmitted to hearing interface device  1710  and produced for user  100 . Thus, in the conditioned audio signal, sound  1820  may be easier to hear to user  100 , louder and/or more easily distinguishable than sounds  1821  and  1822 , which may represent background noise within the environment. 
       FIG. 19  is a flowchart showing an exemplary process  1900  for selectively amplifying sounds emanating front a detected look direction of a user consistent with disclosed embodiments. Process  1900  may be performed by one or more processors associated with apparatus  110 , such as processor  210 . In some embodiments, some or all of process  1900  may be performed on processors external to apparatus  110 . In other words, the processor performing process  1900  may be included in u common housing as microphone  1720  and camera  1730 , or may be included in a second housing. For example, one or more portions of process  1900  may be performed by processors in hearing interface device  1710 , or an auxiliary device, such as computing device  120 . 
     In step  1910 , process  1900  may include receiving a plurality of images from an environment of a user captured by a camera. The camera may be a wearable camera such as camera  1730  of apparatus  110 . In step  1912 , process  1900  may include receiving audio signals representative of sounds received by at least one microphone. The microphone may be configured to capture sounds from an environment of the user. For example, the microphone may be microphone  1720 , as described above. Accordingly, the microphone may include a directional microphone, a microphone array, a multi-port microphone, or various other types of microphones. In some embodiments, the microphone and wearable camera may be included in a common housing, such its the housing of apparatus  110 . The one or more processors performing process  1900  may also be included in the housing or may be included in a second housing. In such embodiments, the processors) may be configured to receive images and/or audio signals from the common housing via a wireless link (e.g., Bluetooth™, NFC, etc.). Accordingly, the common housing (e.g., apparatus  110 ) and the second housing (e.g., computing device  120 ) may further comprise transmitters or various other communication components. 
     In step  1914 , process  1900  may include determining a look direction for the user bused on analysis of at least one of the plurality of images. As discussed above, various techniques may be used to determine the user look direction. In some embodiments, the look direction may be determined based, at least in part, upon detection of a representation of a chin of a user in one or more images. The images may be processed to determine a pointing direction of the chin relative to an optical axis of the wearable camera, as discussed above.  
     In step  1916 , process  1900  may include causing selective conditioning of at least one audio signal received by the at least one microphone from a region associated with the look direction of the user. As described above, the region may be determined based on the user look direction determined in step  1914 . The range may be associated with an angular width about the look direction (e.g., 10 degrees, 20 degrees, 45 degrees, etc.). Various forms of conditioning may be performed on the audio signal, as discussed above. In some embodiments, conditioning may include changing the tone or playback speed of an audio signal. For example, conditioning may include changing a rate of speech associated with the audio signal. In some embodiments, the conditioning may include amplification of the audio signal relative to other audio signals received from outside of the region associated with the look direction of the user. Amplification may be performed by various means, such as operation of a directional microphone configured to focus on audio sounds emanating from the region, or varying one or more parameters associated with the microphone to cause the microphone to focus on audio sounds emanating from the region. The amplification may include attenuating or suppressing one or more audio signals received by the microphone from directions outside the region associated with the look direction of user  110 . 
     In step  1918 , process  1900  may include causing transmission of the at least one conditioned audio signal to a hearing interface device configured to provide sound to an ear of the user. The conditioned audio signal, for example, may be transmitted to hearing interface device  1710 , which may provide sound corresponding to the audio signal to user  100 . The processor performing process  1900  may further be configured to cause transmission to the hearing interface device of one or more audio signals representative of background noise, which may be attenuated relative to the at least one conditioned audio signal. For example, processor  220  may be configured to transmit audio signals corresponding to sounds  1820 ,  1821 , and  1822 . The signal associated with  1820 , however, may be modified in a different manner, for example amplified, from sounds  1821  and  1822  based on a determination that sound  1820  is within region  1830 . In some embodiments, hearing interface device  1710  may include a speaker associated with an earpiece. For example, hearing interlace device may be inserted at least partially into the ear of the user for providing audio to the user. Hearing interface device may also be external to the ear, such as a behind-the-ear hearing device, one or more headphones, a small portable speaker, or the like. In some embodiments, hearing interface device may include a bone conduction microphone, configured to provide an audio signal to user through vibrations of a bone of the user&#39;s head. Such devices may be placed in contact with the exterior of the user&#39;s skin, or may be implanted surgically and attached to the bone of the user. 
     Hearing Aid with Voice and/or Image Recognition  
     Consistent with the disclosed embodiments, a hearing aid may selectively amplify audio signals associated with a voice of a recognized individual. The hearing aid system may store voice characteristics and/or facial features of a recognized person to aid in recognition and selective amplification. For example, when an individual enters the field of view of apparatus  110 , the individual may be recognized as an individual that has been introduced to the device, or that has possibly interacted with user  100  in the past (e.g., a friend, colleague, relative, prior acquaintance, etc.). Accordingly, audio signals associated with the recognized individual&#39;s voice may be isolated and/or selectively amplified relative to other sounds in the environment of the user. Audio signals associated with sounds received from directions other than the individual&#39;s direction may be suppressed, attenuated, filtered or the like. 
     User  100  may wear a hearing aid device similar to the camera-based hearing aid device discussed above. For example, the hearing aid device may be hearing interface device  1720 , as shown in  FIG. 17A . Hearing interface device  1710  may be any device configured to provide audible feedback to user  100 . Hearing interface device  1710  may be placed in one or both cars of user  100 , similar to traditional hearing interface devices. As discussed above, hearing interface device  1710  may be of various styles, including in-the-canal, completely-in-canal, in-the-ear, behind-the-ear, on-the-ear, receiver-in-canal, open fit, or various other styles. Hearing interface device  1710  may include one or more speakers for providing audible feedback to user  100 , a communication unit for receiving signals from another system, such as apparatus  110 , microphones for detecting sounds in the environment of user  100 , internal electronics, processors, memories, etc. Hearing interface device  1710  may correspond to feedback outputting unit  230  or may be separate from feedback outputting unit  230  and may be configured to receive signals from feedback outputting unit  230 . 
     In some embodiments, hearing interface device  1710  may comprise a bone conduction headphone  1711 , as shown in  FIG. 17A . Bone conduction headphone  1711  may be surgically implanted and may provide audible feedback to user  100  through bone conduction of sound vibrations to the inner ear. Hearing interface device  1710  may also comprise one or more headphones (e.g., wireless headphones, over-ear headphones, etc.) or a portable speaker carried or worn by user  100 . In some embodiments, hearing interlace device  1710  may be integrated into other devices, such as a Bluetooth™ headset of the user, glasses, a helmet (e.g., motorcycle helmets, bicycle helmets, etc.), a hat, etc. 
     Hearing interface device  1710  may be configured to communicate with a camera device, such as apparatus  110 . Such communication may be through a wired connection, or may be made wirelessly (e.g., using a Bluetooth™, NFC, or forms of wireless communication). As discussed above, apparatus  110  may be worn by user  100  in various configurations, including  being physically connected to a shirt, necklace, a bell, glasses, a wrist strap, a button, or other articles associated with user  100 . In some embodiments, one or more additional devices may also be included, such as computing device  120 . Accordingly, one or more of the processes or functions described herein with respect to apparatus  110  or processor  210  may be performed by computing device  120  and/or processor  540 . 
     As discussed above, apparatus  110  may comprise at least one microphone and at least one image capture device. Apparatus  110  may comprise microphone  1720 , as described with respect to  FIG. 17B . Microphone  1720  may be configured to determine a directionality of sounds in the environment of user  100 . For example, microphone  1720  may comprise one or more directional microphones, a microphone array, a multi-port microphone, or the like. The microphones shown in  FIG. 17B  are by way of example only, and any suitable number, configuration, or location of microphones may be utilized. Processor  210  may be configured to distinguish sounds within the environment of user  100  and determine an approximate directionality of each sound. For example, using an array of microphones  1720 , processor  210  may compare the relative timing or amplitude of an individual sound among the microphones  1720  to determine a directionality relative to apparatus  100 . Apparatus  110  may comprise one or more cameras, such as camera  1730 , which may correspond to image sensor  220 . Camera  1730  may be configured to capture images of the surrounding environment of user  100 . 
     Apparatus  110  may be configured to recognize an individual in the environment of user  100 .  FIG. 20A  is a schematic illustration showing an exemplary environment for use of a hearing aid with voice and/or image recognition consistent with the present disclosure. Apparatus  110  may be configured to recognize a face  2011  or voice  2012  associated with an individual  2010  within the environment of user  100 . For example, apparatus  110  may be configured to capture one or more images of the surrounding environment of user  100  using camera  1730 . The captured images may include a representation of a recognized individual  2010 , which may be a friend, colleague, relative, or prior acquaintance of user  100 . Processor  210  (and/or processors  210   a  and  210   b ) may be configured to analyze the captured images and detect the recognized user using various facial recognition techniques, as represented by element  2011 . Accordingly, apparatus  110 , or specifically memory  550 , may comprise one or more facial or voice recognition components. 
       FIG. 20B  illustrates an exemplary embodiment of apparatus  110  comprising facial and voice recognition components consistent with the present disclosure. Apparatus  110  is shown in  FIG. 20B  in a simplified form, and apparatus  110  may contain additional elements or may have alternative configurations, for example, as shown in  FIGS. 5A-5C . Memory  550  (or  550   a  or  550   b ) may include facial recognition component  2040  and voice recognition component   2041 . These components may be instead of or in addition to orientation identification module  601 , orientation adjustment module  602 , and motion tracking module  603  as shown in  FIG. 6 . Components  2040  and  2041  may contain software instructions for execution by at least one processing device, e.g., processor  210 , included with a wearable apparatus. Components  2040  and  2041  are shown within memory  550  by way of example only, and may be located in other locations within the system. For example, components  2040  and  2041  may be located in hearing interface device  1710 , in computing device  120 , on a remote server, or in another associated device. 
     Facial recognition component  2040  may be configured to identify one or more faces within the environment of user  100 . For example, facial recognition component  2040  may identify facial features on the face  2011  of individual  2010 , such as the eyes, nose, cheekbones, jaw, or other features. Facial recognition component  2040  may then analyze the relative size and position of these features to identify the user. Facial recognition component  2040  may utilize one or more algorithms for analyzing the detected features, such as principal component analysis (e.g., using eigenfaces), linear discriminant analysis, elastic bunch graph matching (e.g., using Fisherface), Local Binary Patterns Histograms (LBPH), Scale Invariant Feature Transform (SIFT), Speed Up Robust Features (SURF), or the like. Other facial recognition techniques such as 3-Dimensional recognition, skin texture analysis, and/or thermal imaging may also be used to identify individuals. Other features besides facial features may also be used for identification, such as the height, body shape, or other distinguishing features of individual  2010 . 
     Facial recognition component  2040  may access a database or data associated with user  100  to determine if the detected facial features correspond to a recognized individual. For example, a processor  210  may access a database  2050  containing information about individuals known to user  100  and data representing associated facial features or other identifying features. Such data may include one or more images of the individuals, or data representative of a face of the user that may be used for identification through facial recognition. Database  2050  may be and device capable of storing information about one or more individuals, and may include a hard drive, a solid state drive, a web storage platform, a remote server, or the like. Database  2050  may be located within apparatus  110  (e.g., within memory  550 ) or external to apparatus  110 , as shown in  FIG. 20B . In some embodiments, database  2050  may be associated with a social network platform, such as Facebook™, LinkedIn™, Instagram™, etc. Facial recognition component  2040  may also access a contact list of user  100 , such as a contact list on the use&#39;s phone, a web-based contact list (e.g., through Outlook™, Skype™, Google™, SalesForce™, etc.) or a dedicated contact list associated with hearing interface device  1710 . In some embodiments, database  2050  may be compiled by apparatus  110  through previous facial recognition analysis. For example, processor  210  may be configured to store data associated with one or more faces recognized in images captured by apparatus  110  in database  2050 . Each time a face is detected in the images, the detected facial features or other data may be compared to previously identified faces in database  2050 . Facial recognition component  2040  may determine that an individual is a recognized individual of user  100  if the individual has previously been recognized by the system in a number of instances exceeding a certain threshold, if the individual has been explicitly introduced to apparatus  110 , or the like. 
     In some embodiments, user  100  may have access to database  2050 , such as through a web interface, an application on a mobile device, or through apparatus  110  or an associated device. For example, user  100  may be able to select which contacts are recognizable by apparatus  110  and/or delete or add certain contacts manually. In some embodiments, a user or administrator may be able to train facial recognition component  2040 . For example, user  100  may have an option to confirm or reject identifications made by facial recognition component  2040 , which may improve the accuracy of the system. This training may occur in real time, as individual  2010  is being recognized, or at some later time. 
     Other data or information may also inform the facial identification process. In some embodiments, processor  210  may use various techniques to recognize the voice of individual  2010 , as described in further detail below. The recognized voice patient and the detected facial features may be used, either alone or in combination, to determine that individual  2010  is recognized by apparatus  110 . Processor  210  may also determine a user look direction  1750 , as described above, which may be used to verify the identity of individual  2010 . For example, if user  100  is looking in the direction of individual  2010  (especially for a prolonged period), this may indicate that individual  2010  is recognized by user  100 , which may be used to increase the confidence of facial recognition component  2040  or other identification means. 
     Processor  210  may further be configured to determine whether individual  2010  is recognized by user  100  based on one or more detected audio characteristics of sounds associated with a voice of individual  2010 . Returning to  FIG. 20A , processor  210  may determine that sound  2020  corresponds to voice  2012  of user  2010 . Processor  210  may analyze audio signals representative of sound  2020  captured by microphone  1720  to determine whether individual  2010  is recognized by user  100 . This may be performed using voice recognition component  2041  ( FIG. 20B ) and may include one or more voice recognition algorithms, such as Hidden Markov Models, Dynamic Time Warping, neural networks, or other techniques. Voice recognition component and/or processor  210  may access database  2050 , which may further include a voiceprint of one or more individuals. Voice recognition component  2041  may analyze the audio signal representative of sound  2020  to determine whether voice  2012  matches  a voiceprint of an individual in database  2050 . Accordingly, database  2050  may contain voiceprint data associated with a number of individuals, similar to the stored facial identification data described above. After determining a match, individual  2010  may be determined to be a recognized individual of user  100 . This process may be used alone, or in conjunction with the facial recognition techniques described above. For example, individual  2010  may be recognized using facial recognition component  2040  and may be verified using voice recognition component  2041 , or vice versa. 
     In some embodiments, apparatus  110  may detect the voice of an individual that is not within the field of view of apparatus  110 . For example, the voice may be heard over a speakerphone, from a back seat, or the like. In such embodiments, recognition of an individual may be based on the voice of the individual only, in the absence of a speaker in the field of view. Processor  110  may analyze the voice of the individual as described above, for example, by determining whether the detected voice matches a voiceprint of an individual in database  2050 . 
     After determining that individual  2010  is a recognized individual of user  100 . processor  210  may cause selective conditioning of audio associated with the recognized individual. The conditioned audio signal may be transmitted to hearing interface dev ice  1710 , and thus may provide user  100  with audio conditioned based on the recognized individual. For example, the conditioning may include amplifying audio signals determined to correspond to sound  2020  (which may correspond to voice  2012  of individual  2010 ) relative to other audio signals. In some embodiments, amplification may be accomplished digitally, for example by processing audio signals associated with sound  2020  relative to other signals. Additionally, or alternatively, amplification may be accomplished by changing one or more parameters of microphone  1720  to focus on audio sounds associated with individual  2010 . For example, microphone  1720  may be a directional microphone and processor  210  may perform an operation to focus microphone  1720  on sound  2020 . Various other techniques for amplifying sound  2020  may be used, such as using a beamforming microphone array, acoustic telescope techniques, etc. 
     In some embodiments, selective conditioning may include attenuation or suppressing one or more audio signals received from directions not associated with individual  2010 . For example, processor  210  may attenuate sounds  2021  and/or  2022 . Similar to amplification of sound  2020 , attenuation of sounds may occur through processing audio signals, or by varying one or more parameters associated with microphone  1720  to direct focus away from sounds not associated with individual  2010 . 
     Selective conditioning may further include determining whether individual  2010  is speaking. For example, processor  210  may be configured to analyze images or videos containing representations of individual  2010  to determine when individual  2010  is speaking, for  example, based on detected movement of the recognized individual&#39;s lips. This may also be determined through analysis of audio signals received by microphone  1720 , for example by detecting the voice  2012  of individual  2010 . In some embodiments, the selective conditioning may occur dynamically (initiated and/or terminated) based on whether or not the recognized individual is speaking. 
     In some embodiments, conditioning may further include changing a tone of one or more audio signals corresponding to sound  2020  to make the sound more perceptible to user  100 . For example, user  100  may have lesser sensitivity to tones in a certain range and conditioning of the audio signals may adjust the pitch of sound  2020 . In some embodiments processor  210  may be configured to change a rate of speech associated with one or more audio signals. For example, sound  2020  may be determined to correspond to voice  2012  of individual  2010 . Processor  210  may be configured to vary the rate of speech of individual  2010  to make the detected speech more perceptible to user  100 . Various other processing may be performed, such as modifying the tone of sound  2020  to maintain the same pitch as the original audio signal, or to reduce noise within the audio signal. 
     In some embodiments, processor  210  may determine a region  2030  associated with individual  2010 . Region  2030  may be associated with a direction of individual  2010  relative to apparatus  110  or user  100 . The direction of individual  2010  may be determined using camera  1730  and/or microphone  1720  using the methods described above. As shown in  FIG. 20A , region  2030  may be defined by a cone or range of directions based on a determined direction of individual  2010 . The range of angles may be defined by an angle, θ, as shown in  FIG. 20A . The angle, θ, may be any suitable angle for defining a range for conditioning sounds within the environment of user  100  (e.g., 10 degrees, 20 degrees, 45 degrees). Region  2030  may be dynamically calculated as the position of individual  2010  changes relative to apparatus  110 . For example, as user  100  turns, or if individual  1020  moves within the environment, processor  210  may be configured to track individual  2010  within the environment and dynamically update region  2030 . Region  2030  may be used for selective conditioning, for example by amplifying sounds associated with region  2030  and/or attenuating sounds determined to be emanating from outside of region  2030 . 
     The conditioned audio signal may then be transmitted to hearing interface device  1710  and produced for user  100 . Thus, in the conditioned audio signal, sound  2020  (and specifically voice  2012 ) may be louder and/or more easily distinguishable than sounds  2021  and  2022 , which may represent background noise within the environment. 
     In some embodiments, processor  210  may perform further analysis based on captured images or videos to determine how to selectively condition audio signals associated  with a recognized individual. In some embodiments, processor  210  may analyze the captured images to selectively condition audio associated with one individual relative to others. For example, processor  210  may determine the direction of a recognized individual relative to the user based on the images and may determine how to selectively condition audio signals associated with the individual based on the direction. If the recognized individual is standing to the front of the user, audio associated with that user may be amplified (or otherwise selectively conditioned) relative to audio associated with an individual standing to the side of the user. Similarly, processor  210  may selectively condition audio signals associated with an individual based on proximity to the user. Processor  210  may determine a distance from the user to each individual based on captured images and may selectively condition audio signals associated with the individuals based on the distance. For example, an individual closer to the user may be prioritized higher than an individual that is farther away. 
     In some embodiments, selective conditioning of audio signals associated with a recognized individual may be based on the identities of individuals within the environment of the user. For example, where multiple individuals are detected in the images, processor  210  may use one or more facial recognition techniques to identify the individuals, as described above. Audio signals associated with individuals that are known to user  100  may be selectively amplified or otherwise conditioned to have priority over unknown individuals. For example, processor  210  may be configured to attenuate or silence audio signals associated with bystanders in the user&#39;s environment, such as a noisy office mate, etc. In some embodiments, processor  210  may also determine a hierarchy of individuals and give priority based on the relative status of the individuals. This hierarchy may be based on the individual&#39;s position within a family or an organization (e.g., a company, sports team, club, etc.) relative to the user. For example, the user&#39;s boss may be ranked higher than a co-worker or a member of the maintenance staff and thus may have priority in the selective conditioning process. In some embodiments, the hierarchy may be determined based on a list or database. Individuals recognized by the system may be ranked individually or grouped into tiers of priority. This database may be maintained specifically for this purpose, or may be accessed externally. For example, the database may be associated with a social network of the user (e.g., Facebook™, LinkedIn™, etc.) and individuals may be prioritized based on their grouping or relationship with the user. Individuals identified as “close friends” or family, for example, may be prioritized over acquaintances of the user. 
     Selective conditioning may be based on a determined behavior of one or more individuals determined based on the captured images. In some embodiments, processor  210  may be configured to determine a look direction of the individuals in the images. Accordingly, the selective conditioning may be based on behavior of the other individuals towards the recognized  individual. For example, processor  210  may selectively condition audio associated with a first individual that one or more other users are looking at. If the attention of the individuals shifts to a second individual, processor  210  may then switch to selectively condition audio associated with the second user. In some embodiments, processor  210  may be configured to selectively condition audio based on whether a recognized individual is speaking to the user or to another individual. For example, when the recognized individual is speaking to the user, the selective conditioning may include amplifying an audio signal associated with the recognized individual relative to other audio signals received from directions outside a region associated with the recognized individual. When the recognized individual is speaking to another individual, the selective conditioning may include attenuating the audio signal relative to other audio signals received from directions outside the region associated with the recognized individual. 
     In some embodiments, processor  210  may have access to one or more voiceprints of individuals, which may facilitate selective conditioning of voice  2012  of individual  2010  in relation to other sounds or voices. Having a speaker&#39;s voiceprint, and a high quality voiceprint in particular, may provide for fast and efficient speaker separation. A high quality voice print may be collected, for example, when the user speaks alone, preferably in a quiet environment. By having a voiceprint of one or more speakers, it is possible to separate an ongoing voice signal almost in real time, e.g. with a minimal delay, using a sliding time window. The delay may be, for example 10 ms, 20 ms, 30 ms, 50 ms, 100 ms, or the like. Different time windows may be selected, depending on the quality of the voice print, on the quality of the captured audio, the difference in characteristics between the speaker and other speaker(s), the available processing resources, the required separation quality, or the like. In some embodiments, a voice print may be extracted from a segment of a conversation in which an individual speaks alone, and then used for separating the individual&#39;s voice later in the conversation, whether the individual&#39;s is recognized or not. 
     Separating voices may be performed as follows: spectral features, also referred to as spectral attributes, spectral envelope, or spectrogram may be extracted from a clean audio of a single speaker and fed into a pre-trained first neural network, which generates or updates a signature of the speaker&#39;s voice based on the extracted features. The audio may be for example, of one second of clean voice. The output signature may be a vector representing the speaker&#39;s voice, such that the distance between the vector and another vector extracted from the voice of the same speaker is typically smaller than the distance between the vector and a vector extracted from the voice of another speaker. The speaker&#39;s model may be pre-generated from a captured audio. Alternatively or additionally, the model may be generated after a segment of the audio in  which only the speaker speaks, followed by another segment in which the speaker and another speaker (or background noise) is heard, and which it is required to separate. 
     Then, to separate the speaker&#39;s voice from additional speakers or background noise in a noisy audio, a second pro-trained neural network may receive the noisy audio and the speaker&#39;s signature, and output an audio (which may also be represented as attributes) of the voice of the speaker as extracted from the noisy audio, separated from the other speech or background noise. It will be appreciated that the same or additional neural networks may be used to separate the voices of multiple speakers. For example, if there are two possible speakers, two neural networks may be activated, each with models of the same noisy output and one of the two speakers. Alternatively, a neural network may receive voice signatures of two or more speakers, and output the voice of each of the speakers separately. Accordingly, the system may generate two or more different audio outputs, each comprising the speech of the respective speaker. In some embodiments, if separation is impossible, the input voice may only be cleaned from background noise. 
       FIG. 21  is a flowchart showing an exemplary process  2100  for selectively amplifying audio signals associated with a voice of a recognized individual consistent with disclosed embodiments. Process  2100  may be performed by one or more processors associated with apparatus  110 , such as processor  210 . In some embodiments, some or all of process  2100  may be performed on processors external to apparatus  110 . In other words, the processor performing process  2100  may be included in the same common housing as microphone  1720  and camera  1730 , or may be included in a second housing. For example, one or more portions of process  2100  may be performed by processors in hearing interlace device  1710 , or in an auxiliary device, such as computing device  120 . 
     In step  2110 , process  2100  may include receiving a plurality of images from an environment of a user captured by a camera. The images may be captured by a wearable camera such as camera  1730  of apparatus  110 . In step  2112 , process  2100  may include identifying a representation of a recognized individual in at least one of the plurality of images. Individual  2010  may be recognized by processor  210  using facial recognition component  2040 , as described above. For example, individual  2010  may be a friend, colleague, relative, or prior acquaintance of the user. Processor  210  may determine whether an individual represented in at least one of the plurality of images is a recognized individual based on one or more detected facial features associated with the individual. Processor  210  may also determine whether the individual is recognized based on one or more detected audio characteristics of sounds determined to be associated with a voice of the individual, as described above.  
     In step  2114 , process  2100  may include receiving audio signals representative of sounds captured by a microphone. For example, apparatus  110  may receive audio signals representative of sounds  2020 .  2021 , and  2022 , captured by microphone  1720 . Accordingly, the microphone may include a directional microphone, a microphone array, a multi-port microphone, or various other types of microphones, as described above. In some embodiments, the microphone and wearable camera may be included in a common housing, such as the housing of apparatus  110 . The one or more processors performing process  2100  may also be included in the housing (e.g., processor  210 ), or may be included in a second housing. Where a second housing is used, the processors) may be configured to receive images and/or audio signals from the common housing via a wireless link (e.g.. Bluetooth™, NFC, etc.). Accordingly, the common housing (e.g., apparatus  110 ) and the second housing (e.g., computing device  120 ) may further comprise transmitters, receivers, and or various other communication components. 
     In step  2116 , process  2100  may include cause selective conditioning of at least one audio signal received by the at least one microphone from a region associated with the at least one recognized individual. As described above, the region may be determined based on a determined direction of the recognized individual based one or more of the plurality of images or audio signals. The range may be associated with an angular width about the direction of the recognized individual (e.g., 10 degrees, 20 degrees, 45 degrees, etc.). 
     Various forms of conditioning may be performed on the audio signal, as discussed above. In some embodiments, conditioning may include changing the tone or playback speed of an audio signal. For example, conditioning may include changing a rate of speech associated with the audio signal. In some embodiments, the conditioning may include amplification of the audio signal relative to other audio signals received from outside of the region associated with the recognized individual. Amplification may be performed by various means, such its operation of a directional microphone configured to focus on audio sounds emanating from the region or varying one or more parameters associated with the microphone to cause the microphone to focus on audio sounds emanating from the region. The amplification may include attenuating or suppressing one or more audio signals received by the microphone from directions outside the region. In some embodiments, step  2116  may further comprise determining, based on analysis of the plurality of images, that the recognized individual is speaking and trigger the selective conditioning based on the determination that the recognized individual is speaking. For example, the determination that the recognized individual is speaking may be based on detected movement of the recognized individual&#39;s lips. In some embodiments, selective conditioning may be based on further analysts of the captured images as described above, for example, based  on the direction or proximity of the recognized individual, the identity of the recognized individual, the behavior of other individuals, etc. 
     In step  2118 , process  2100  may include causing transmission of the at least one conditioned audio signal to a hearing interface device configured to provide sound to an car of the user. The conditioned audio signal, for example, may be transmitted to hearing interface device  1710 , which may provide sound corresponding to the audio signal to user  100 . The processor performing process  2100  may further be configured to cause transmission to the hearing interface device of one or more audio signals representative of background noise, which may be attenuated relative to the at least one conditioned audio signal. For example, processor  210  may be configured to transmit audio signals corresponding to sounds  2020 ,  2021 , and  2022 . The signal associated with  2020 , however, may be amplified in relation to sounds  2021  and  2022  based on a determination that sound  2020  is within region  2030 . In some embodiments, hearing interface device  1710  may include a speaker associated with an earpiece. For example, hearing interlace device  1710  may be inserted at least partially into the ear of the user for providing audio to the user. Hearing interface device may also be external to the ear, such as a behind-the-ear hearing device, one or more headphones, a small portable speaker, or the like. In some embodiments, hearing interface device may include a bone conduction microphone, configured to provide an audio signal  10  user through vibrations of a bone of the user&#39;s head. Such devices may be placed in contact with the exterior of the user&#39;s skin, or may be implanted surgically and attached to the bone of the user. 
     In addition to recognizing voices of individuals speaking to user  100 , the systems and methods described above may also be used to recognize the voice of user  100 . For example, voice recognition unit  2041  may be configured to analyze audio signals representative of sounds collected from the user&#39;s environment to recognize the voice of user  100 . Similar to the selective conditioning of the voice of recognized individuals, the voice of user  100  may be selectively conditioned. For example, sounds may be collected by microphone  1720 , or by a microphone of another device, such as a mobile phone (or a device linked to a mobile phone). Audio signals corresponding to the voice of user  100  may be selectively transmitted to a remote device, for example, by amplifying the voice of user  100  and/or attenuating or eliminating altogether sounds other than the user&#39;s voice. Accordingly, a voiceprint of one or more users of apparatus  110  may be collected and/or stored to facilitate detection and/or isolation of the user&#39;s voice, as described in further detail above. 
       FIG. 22  is a flowchart showing an exemplary process  2200  for selectively transmitting audio signals associated with a voice of a recognized user consistent with disclosed  embodiments. Process  2200  may be performed by one or more processors associated with apparatus  110 , such as processor  210 . 
     In stop  2210 , process  2200  may include receiving audio signals representative of sounds captured by a microphone. For example, apparatus  110  may receive audio signals representative of sounds  2020 ,  2021 , and  2022 , captured by microphone  1720 . Accordingly, the microphone may include a directional microphone, a microphone array, a multi-port microphone, or various other types of microphones, as described above. In step  2212 , process  2200  may include identifying, based on analysis of the received audio signals, one or more voice audio signals representative of a recognized voice of the user. For example, the voice of the user may be recognized based on a voiceprint associated with the user, which may be stored in memory  550 , database  2050 , or other suitable locations. Processor  210  may recognize the voice of the user, for example, using voice recognition component  2041 . Processor  210  may separate an ongoing voice signal associated with the user almost in real time, e.g. with a minimal delay, using a sliding time window. The voice may be separated by extracting spectral features of an audio signal according to the methods described above. 
     In step  2214 , process  2200  may include causing transmission, to a remotely located device, of the one or more voice audio signals representative of the recognized voice of the user. The remotely located device may be any device configured to receive audio signals remotely, either by a wired or wireless form of communication. In some embodiments, the remotely located device may be another device of the user, such as a mobile phone, an audio interface device, or another form of computing device. In some embodiments, the voice audio signals may be processed by the remotely located device and/or transmitted further. In step  2216 , process  2200  may include preventing transmission, to the remotely located device, of at least one background noise audio signal different from the one or more voice audio signals representative of a recognized voice of the user. For example, processor  210  may attenuate and/or eliminate audio signals associated with sounds  2020 ,  2021 , or  2023 , which may represent background noise. The voice of the user may be separated from other noises using the audio processing techniques described above. 
     In an exemplary illustration, the voice audio signals may be captured by a headset or other device worn by the user. The voice of the user may be recognized and isolated from the background noise in the environment of the user. The headset may transmit the conditioned audio signal of the user&#39;s voice to a mobile phone of the user. For example, the user may be on a telephone call and the conditioned audio signal may be transmitted by the mobile phone to a recipient of the call. The voice of the user may also be recorded by the remotely located device. The audio signal, for example, may be stored on a remote server or other computing device. In  some embodiments, the remotely located device may process the received audio signal, for example, to convert the recognized user&#39;s voice into text. 
     Lip-Tracking Hearing Aid 
     Consistent with the disclosed embodiments, a hearing aid system may selectively amplify audio signals based on tracked lip movements. The hearing aid system analyzes captured images of the environment of a user to detect lips of an individual and track movement of the individual&#39;s lips. The tracked lip movements may serve as a cue for selectively amplifying audio received by the hearing aid system. For example, voice signals determined to sync with the tracked lip movements or that are consistent with the tracked lip movements may be selectively amplified or otherwise conditioned. Audio signals that are not associated with the detected lip movement may be suppressed, attenuated, filtered or the like. 
     User  100  may wear a hearing aid device consistent with the camera-based hearing aid device discussed above. For example, the hearing aid device may be hearing interface device  1710 , as shown in  FIG. 17A . Hearing interface device  1710  may be any device configured to provide audible feedback to user  100 . Hearing interface device  1710  may be placed in one or both cars of user  100 , similar to traditional hearing interface devices. As discussed above, hearing interface device  1710  may be of various styles, including in-the-canal, completely-in-canal, in-the-ear, behind-the-ear, on-the-ear, receiver-in-canal, open fit, or various other styles. Hearing interface device  1710  may include one or more speakers for providing audible feedback to user  100 , microphones for detecting sounds in the environment of user  100 , internal electronics, processors, memories, etc. In some embodiments, in addition to or instead of a microphone, hearing interface device  1710  may comprise one or more communication units, and one or more receivers for receiving signals from apparatus  110  and transferring the signals to user  100 . Hearing interface device  1710  may correspond to feedback outputting unit  230  or may be separate from feedback outputting unit  230  and may be configured to receive signals from feedback outputting unit  230 . 
     In some embodiments, hearing interface device  1710  may comprise a bone conduction headphone  1711 , as shown in  FIG. 17A . Bone conduction headphone  1711  may be surgically implanted and may provide audible feedback to user  100  through hone conduction of sound vibrations to the inner ear. Hearing interface device  1710  may also comprise one or more headphones (e.g., wireless headphones, over-ear headphones, etc.) or a portable speaker carried or worn by user  100 . In some embodiments, hearing interface device  1710  may be integrated into other devices, such as a Bluetooth™ headset of the user, glasses, a helmet (e.g., motorcycle helmets, bicycle helmets, etc.), a hat, etc.  
     Hearing interface device  1710  may be configured to communicate with a camera device, such as apparatus  110 . Such communication may be through a wired connection, or may be made wirelessly (e.g., using a Bluetooth™, NFC, or forms of wireless communication). As discussed above, apparatus  110  may be worn by user  100  in various configurations, including being physically connected to a shirt, necklace, a belt, glasses, a wrist strap, a button, or other articles associated with user  100 . In some embodiments, one or more additional devices may also be included, such as computing device  120 . Accordingly, one or more of the processes or functions described herein with respect to apparatus  110  or processor  210  may be performed by computing device  120  and/or processor  540 . 
     As discussed above, apparatus  110  may comprise at least one microphone and at least one image capture device. Apparatus  110  may comprise microphone  1720 , as described with respect to  FIG. 17B . Microphone  1720  may be configured to determine a directionality of sounds in the environment of user  100 . For example, microphone  1720  may comprise one or more directional microphones, a microphone array, a multi-port microphone, or the like. Processor  210  may be configured to distinguish sounds within the environment of user  100  and determine an approximate directionality of each sound. For example, using an array of microphones  1720 , processor  210  may compare the relative timing or amplitude of an individual sound among the microphones  1720  to determine a directionality relative to apparatus  100 . Apparatus  110  may comprise one or more cameras, such as camera  1730 , which may correspond to image sensor  220 . Camera  1730  may be configured to capture images of the surrounding environment of user  100 . Apparatus  110  may also use one or more microphones of hearing interface device  1710  and accordingly, references to microphone  1720  used herein may also refer to a microphone on hearing interlace device  1710 . 
     Processor  210  (and/or processors  210   a  and  210   b ) may be configured to detect a mouth and/or lips associated with an individual within the environment of user  100 .  FIGS. 23A and 23B  show an exemplary individual  2310  that may be captured by camera  1730  in the environment of a user consistent with the present disclosure. As shown in  FIG. 23 , individual  2310  may be physically present with the environment of user  100 . Processor  210  may be configured to analyze images captured by camera  1730  to detect a representation of individual  2310  in the images. Processor  210  may use a facial recognition component, such as facial recognition component  2040 , described above, to detect and identify individuals in the environment of user  100 . Processor  210  may be configured to detect one or more facial features of user  2310 , including a mouth  2311  of individual  2310 . Accordingly, processor  210  may use one or more facial recognition and/or feature recognition techniques, as described further below.  
     In some embodiments, processor  210  may detect a visual representation of individual  2310  from the environment of user  100 , such as a video of user  2310 . As shown in  FIG. 23B , user  2310  may be detected on the display of a display device  2301 . Display device  2301  may be any device capable of displaying a visual representation of an individual. For example, display device may be a personal computer, a laptop, a mobile phone, a tablet, a television, a movie screen, a handheld gaming device, a video conferencing device (e.g., Facebook Portal™, etc.), a baby monitor, etc. The visual representation of individual  2310  may be a live video feed of individual  2310 , such as a video call, a conference call, a surveillance video, etc. In other embodiments, the visual representation of individual  2310  may be a prerecorded video or image, such as a video message, a television program, or a movie. Processor  210  may detect one or more facial features based on the visual representation of individual  2310 , including a mouth  2311  of individual  2310 . 
       FIG. 23C  illustrates an exemplary lip-tracking system consistent with the disclosed embodiments. Processor  210  may be configured to detect one or more facial features of individual  2310 , which may include, but is not limited to the individual&#39;s mouth  2311 . Accordingly, processor  210  may use one or more image processing techniques to recognize facial features of the user, such as convolutional neural networks (CNN), scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) features, or other techniques. In some embodiments, processor  210  may be configured to detect one or more points  2320  associated with the mouth  2311  of individual  2310 . Points  2320  may represent one or more characteristic points of an individual&#39;s mouth, such as one or more points along the individual&#39;s lips or the corner of the individual&#39;s mouth. The points shown in  FIG. 23C  are for illustrative purposes only and it is understood that any points for tracking the individual&#39;s lips may be determined or identified via one or more image processing techniques. Points  2320  may be detected at various other locations, including points associated with the individual&#39;s teeth, tongue, cheek, chin, eyes, etc. Processor  210  may determine one or more contours of mouth  2311  (e.g., represented by lines or polygons) based on points  2320  or based on the captured image. The contour may represent the entire mouth  2311  or may comprise multiple contours, for example including a contour representing an upper lip and a contour representing a lower lip. Each lip may also be represented by multiple contours, such as a contour for the upper edge and a contour for the lower edge of each lip. Processor  210  may further use various other techniques or characteristics, such as color, edge, shape or motion detection algorithms to identify the lips of individual  2310 . The identified lips may be tracked over multiple frames or images. Processor  210  may use one or more video tracking algorithms, such as mean-shift tracking, contour  tracking (e.g., a condensation algorithm), or various other techniques. Accordingly, processor  210  may be configured to track movement of the lips of individual  2310  in real time. 
     The tracked lip movement of individual  2310  may be used to separate if required, and selectively condition one or more sounds in the environment of user  100 .  FIG. 24  is a schematic illustration showing an exemplary environment  2400  for use of a lip-tracking hearing aid consistent with the present disclosure. Apparatus  110 , worn by user  100  may be configured to identify one or more individuals within environment  2400 . For example, apparatus  110  may-be configured to capture one or more images of the surrounding environment  2400  using camera  1730 . The captured images may include a representation of individuals  2310  and  2410 , who may be present in environment  2400 . Processor  210  may be configured to detect a mouth of individuals  2310  and  2410  and track their respective lip movements using the methods described above. In some embodiments, processor  210  may further be configured to identify individuals  2310  and  2410 , for example, by detecting facial features of individuals  2310  and  2410  and comparing them to a database, as discussed previously. 
     In addition to detecting images, apparatus  110  may be configured to detect one or more sounds in the environment of user  100 . For example, microphone  1720  may detect one or more sounds  2421 ,  2422 , and  2423  within environment  2400 . In some embodiments, the sounds may represent voices of various individuals. For example, as shown in  FIG. 24 , sound  2421  may represent a voice of individual  2310  and sound  2422  may represent a voice of individual  2410 . Sound  2423  may represent additional voices and/or background noise within environment  2400 . Processor  210  may be configured to analyze sounds  2421 ,  2422 , and  2423  to separate and identify audio signals associated with voices. For example, processor  210  may use one or more speech or voice activity detection (VAD) algorithms and/or the voice separation techniques described above. When there are multiple voices detected in the environment, processor  210  may isolate audio signals associated with each voice. In some embodiments, processor  210  may perform further analysis on the audio signal associated the detected voice activity to recognize the speech of the individual. For example, processor  210  may use one or more voice recognition algorithms (e.g., Hidden Markov Models, Dynamic Time Warping, neural networks, or other techniques) to recognize the voice of the individual. Processor  210  may also be configured to recognize the words spoken by individual  2310  using various speech-to-text algorithms. In some embodiments, instead of using microphone  1710 , apparatus  110  may receive audio signals from another device through a communication component, such as wireless transceiver  530 . For example, if user  100  is on a video call, apparatus  110  may receive an audio signal representing a voice of user  2310  from display device  2301  or another auxiliary device.  
     Processor  210  may determine, based on lip movements and the detected sounds, which individuals in environment  2400  are speaking. For example, processor  2310  may track lip movements associated with mouth  2311  to determine that individual  2310  is speaking. A comparative analysis may be performed between the detected lip movement and the received audio signals. In some embodiments, processor  210  may determine that individual  2310  is speaking based on a determination that mouth  2311  is moving at the same time as sound  2421  is detected. For example, when the lips of individual  2310  stop moving, this may correspond with a period of silence or reduced volume in the audio signal associated with sound  2421 . In some embodiments, processor  210  may be configured to determine whether specific movements of mouth  2311  correspond to the received audio signal. For example, processor  210  may analyze the received audio signal to identify specific phonemes, phoneme combinations or words in the received audio signal. Processor  210  may recognize whether specific lip movements of mouth  2311  correspond to the identified words or phonemes. Various machine learning or deep learning techniques may be implemented to correlate the expected lip movements to the detected audio. For example, a training data set of known sounds and corresponding lip movements may be fed to a machine learning algorithm to develop a model for correlating detected sounds with expected lip movements. Other data associated with apparatus  110  may further be used in conjunction with the detected lip movement to determine and/or verify whether individual  2310  is speaking, such as a look direction of user  100  or individual  2310 , a detected identity of user  2310 , a recognized voiceprint of user  2310 , etc. 
     Based on the detected lip movement, processor  210  may cause selective conditioning of audio associated with individual  2310 . The conditioning may include amplifying audio signals determined to correspond to sound  2421  (which may correspond to a voice of individual  2310 ) relative to other audio signals. In some embodiments, amplification may be accomplished digitally, for example by processing audio signals associated with sound  2421  relative to other signals. Additionally, or alternatively, amplification may be accomplished by changing one or more parameters of microphone  1720  to focus on audio sounds associated with individual  2310 . For example, microphone  1720  may be a directional microphone and processor  210  may perform an operation to focus microphone  1720  on sound  2421 . Various other techniques for amplifying sound  2421  may be used, such as using a beam forming microphone array, acoustic telescope techniques, etc. The conditioned audio signal may be transmitted to hearing interface device  1710 , and thus may provide user  100  with audio conditioned based on the individual who is speaking. 
     In some embodiments, selective conditioning may include attenuation or suppressing one or more audio signals not associated with individual  2310 , such as sounds  2422   and  2423 . Similar to amplification of sound  2421 , attenuation of sounds may occur through processing audio signals, or by varying one or more parameters associated with microphone  1720  to direct focus away from sounds not associated with individual  2310 . 
     In some embodiments, conditioning may further include changing a tone of one or more audio signals corresponding to sound  2421  to make the sound more perceptible to user  100 . For example, user  100  may have lesser sensitivity to tones in a certain range and conditioning of the audio signals may adjust the pitch of sound  2421 . For example, user  100  may experience hearing loss in frequencies above 10 kHz and processor  210  may remap higher frequencies (e.g., at 15 kHz) to 10 kHz. In some embodiments processor  210  may be configured to change a rate of speech associated with one or more audio signals. Processor  210  may be configured to vary the rate of speech of individual  2310  to make the detected speech more perceptible to user  100 . If speech recognition has been performed on the audio signal associated with sound  2421 , conditioning may further include modifying the audio signal based on the detected speech. For example, processor  210  may introduce pauses or increase the duration of pauses between words and or sentences, which may make the speech easier to understand. Various other processing may be performed, such as modifying the tone of sound  2421  to maintain the same pitch as the original audio signal, or to reduce noise within the audio signal. 
     The conditioned audio signal may then be transmitted to hearing interface device  1710  and then produced for user  100 . Thus, in the conditioned audio signal, sound  2421  (may be louder and or more easily distinguishable than sounds  2422  and  2423 . 
     Processor  210  may be configured to selectively condition multiple audio signals based on which individuals associated with the audio signals are currently speaking. For example, individual  2310  and individual  2410  may be engaged in a conversation within environment  2400  and processor  210  may be configured to transition from conditioning of audio signals associated with sound  2421  to conditioning of audio signals associated with sound  2422  based on the respective lip movements of individuals  2310  and  2410 . For example, lip movements of individual  2310  may indicate that individual  2310  has stopped speaking or lip movements associated with individual  2410  may indicate that individual  2410  has started speaking. Accordingly, processor  210  may transition between selectively conditioning audio signals associated with sound  2421  to audio signals associated with sound  2422 . In some embodiments, processor  210  may be configured to process and/or condition both audio signals concurrently but only selectively transmit the conditioned audio to hearing interface device  1710  based on which individual is speaking. Where speech recognition is implemented, processor  210  may determine and/or anticipate a transition between speakers based on the context of the speech. For example, processor  210  may analyze audio signals associate with sound  2421  to  determine that individual  2310  has reached the end of a sentence or has asked a question, which may indicate individual  2310  has finished or is about to finish speaking. 
     In some embodiments, processor  210  may be configured to select between multiple active speakers to selectively condition audio signals. For example, individuals  2310  and  2410  may both be speaking at the same time or their speech may overlap during a conversation. Processor  210  may selectively condition audio associated with one speaking individual relative to others. This may include giving priority to a speaker who has started hut not finished a word or sentence or has not finished speaking altogether when the other speaker started speaking. This determination may also be driven by the context of the speech, as described above. 
     Various other factors may also be considered in selecting among active speakers. For example, a look direction of the user may be determined and the individual in the look direction of the user may be given higher priority among the active speakers. Priority may also be assigned based on the look direction of the speakers. For example, if individual  2310  is looking at user  100  and individual  2410  is looking elsewhere, audio signals associated with individual  2310  may be selectively conditioned. In some embodiments, priority may be assigned based on the relative behavior of other individuals in environment  2400 . For example, if both individual  2310  and individual  2410  are speaking and more other individuals are looking at individual  2410  than individual  2310 , audio signals associated with individual  2410  may be selectively conditioned over those associated with individual  2310 . In embodiments where the identity of the individuals is determined, priority may be assigned based on the relative status of the speakers, as discussed previously in greater detail. User  100  may also provide input into which speakers are prioritized through predefined settings or by actively selecting which speaker to focus on. 
     Processor  210  may also assign priority based on how the representation of individual  2310  is detected. While individuals  2310  and  2410  are shown to be physically present in environment  2400 , one or more individuals may be detected as visual representations of the individual (e.g., on a display device) as shown in  FIG. 23B . Processor  210  may prioritize speakers based on whether or not they are physically present in environment  2400 . For example, processor  210  may prioritize speakers who are physically present over speakers on a display. Alternatively, processor  210  may prioritize a video over speakers in a room, for example, if user  100  is on a video conference or if user  100  is watching a movie. The prioritized speaker or speaker type (e.g. present or not) may also be indicated by user  100 , using a user interface associated with apparatus  110 .  
       FIG. 25  is a flowchart showing an exemplary process  2500  for selectively amplifying audio signals based on tracked lip movements consistent with disclosed embodiments. Process  2500  may be performed by one or more processors associated with apparatus  110 , such as processor  210 . The processor(s) may be included in the same common housing as microphone  1720  and camera  1730 , which may also be used for process  2500 . In some embodiments, some or all of process  2500  may be performed on processors external to apparatus  110 , which may be included in a second housing. For example, one or more portions of process  2500  may be performed by processors in hearing interface device  1710 , or in an auxiliary device, such as computing device  120  or display device  2301 . In such embodiments, the processor may be configured to receive the captured images via a wireless link between a transmitter in the common housing and receiver in the second housing. 
     In step  2510 , process  2500  may include receiving a plurality of images captured by a wearable camera from an environment of the user. The images may be captured by a wearable camera such as camera  1730  of apparatus  110 , In step  2520 , process  2500  may include identifying a representation of at least one individual in at least one of the plurality of images. The individual may be identified using various image detection algorithms, such as Haar cascade, histograms of oriented gradients (HOG), deep convolution neural networks (CNN), scale-invariant feature transform (SIFT), or the like. In some embodiments, processor  210  may be configured to detect visual representations of individuals, for example from a display device, as shown in  FIG. 23B . 
     In step  2530 , process  2500  may include identifying at least one lip movement or lip position associated with a mouth of the individual, based on analysis of the plurality of images. Processor  210  may be configured to identify one or more points associated with the mouth of the individual. In some embodiments, processor  210  may develop a contour associated with the mouth of the individual, which may define a boundary associated with the mouth or lips of the individual. The lips identified in the image may be tracked over multiple frames or images to identify the lip movement. Accordingly, processor  210  may use various video tracking algorithms, as described above. 
     In step  2540 , process  2500  may include receiving audio signals representative of the sounds captured by a microphone from the environment of the user. For example, apparatus  110  may receive audio signals representative of sounds  2421 ,  2422 , and  2423  captured by microphone  1720 . In step  2550 , process  2500  may include identifying, based on analysis of the sounds captured by the microphone, a first audio signal associated with a first voice and a second audio signal associated with a second voice different from the first voice. For example, processor  210  may identify an audio signal associated with sounds  2421  and  2422  representing  the voice of individuals  2310  and  2410 , respectively. Processor  210  may analyze the sounds received from microphone  1720  to separate the first and second voices using any currently known or future developed techniques or algorithms. Step  2550  may also include identifying additional sounds, such as sound  2423  which may include additional voices or background noise in the environment of the user. In some embodiments, processor  210  may perform further analysis on the first and second audio signals, for example, by determining the identity of individuals  2310  and  2410  using available voiceprints thereof. Alternatively, or additionally, processor  210  may use speech recognition tools or algorithms to recognize the speech of the individuals. 
     In step  2560 , process  2500  may include causing selective conditioning of the first audio signal based on a determination that the first audio signal is associated with the identified lip movement associated with the mouth of the individual. Processor  210  may compare the identified lip movement with the first and second audio signals identified in step  2550 . For example, processor  210  may compare the timing of the detected lip movements with the timing of the voice patterns in the audio signals. In embodiments where speech is detected, processor  210  may further compare specific lip movements to phonemes or other features detected in the audio signal, as described above. Accordingly, processor  210  may determine that the first audio signal is associated with the detected lip movements and is thus associated with an individual who is speaking. 
     Various forms of selective conditioning may be performed, as discussed above. In some embodiments, conditioning may include changing the tone or playback speed of an audio signal. For example, conditioning may include remapping the audio frequencies or changing a rate of speech associated with the audio signal. In some embodiments, the conditioning may include amplification of a first audio signal relative to other audio signals. Amplification may be performed by various means, such as operation of a directional microphone, varying one or more parameters associated with the microphone, or digitally processing the audio signals. The conditioning may include attenuating or suppressing one or more audio signals that are not associated with the detected lip movement. The attenuated audio signals may include audio signals associated with other sounds detected in the environment of the user, including other voices such as a second audio signal. For example, processor  210  may selectively attenuate the second audio signal based on a determination that the second audio signal is not associated with the identified lip movement associated with the mouth of the individual. In some embodiments, the processor may be configured to transition from conditioning of audio signals associated with a first individual to conditioning of audio signals  associated with a second individual when identified lip movements of the first individual indicates that the first individual has finished a sentence or has finished speaking. 
     In step  2570 , process  2500  may include causing transmission of the selectively conditioned first audio signal to a hearing interface device configured to provide sound to an car of the user. The conditioned audio signal, for example, may be transmitted to hearing interlace device  1710 , which may provide sound corresponding to the first audio signal to user  100 . Additional sounds such as the second audio signal may also be transmitted. For example, processor  210  may be configured to transmit audio signals corresponding to sounds  2421 ,  2422 , and  2423 . The first audio signal, which may be associated with the detected lip movement of individual  2310 , may be amplified, however, in relation to sounds  2422  and  2423  as described above. In some embodiments, hearing interface  1710  device may include a speaker associated with an earpiece. For example, hearing interface device may be inserted at least partially into the ear of the user for providing audio to the user. Hearing interface device may also the external to the ear, such as a behind-the-ear hearing device, one or more headphones, a small portable speaker, or the like. In some embodiments, hearing interface device may include a bone conduction microphone, configured to provide an audio signal to user through vibrations of a bone of the user&#39;s head. Such devices may be placed in contact with the exterior of the user&#39;s skin, or may be implanted surgically and attached to the hone of the user. 
     Retroactive Processing and Transmission of Words 
     As described above, the disclosed embodiments may include selectively conditioning an audio signal to remove background noise or other sounds and transmit speech or other desired sounds to a hearing interlace device. Accordingly, this may include analyzing an audio signal to determine whether it corresponds to speech (and thus should be transmittal), or background noise or other undesired sound (and thus should not be transmitted). In some embodiments, quickly transmitting audio to a hearing interface device to reduce a delay in transmission may be beneficial. For example, transmitting the voice of another speaker to the hearing interface after a relatively long delay may be unpleasant or distracting to a user. Accordingly, in some embodiments, it may be beneficial to quickly detect and classify sounds within an audio signal to reduce the latency at which desired sounds are transmitted. 
     In some embodiments, by classifying sounds as either being desired (i.e., sounds to be transmitted such as spoken words) or undesired (i.e., sounds not to be transmitted such as noise) more quickly, inaccuracies in the classification may arise. For example, the system may classify sounds as either speech or background noise based on a small sample of an audio signal, which may only include a portion of a word spoken by an individual. The beginning of some words, such as, for example, words starting with “gr-” may sound like a humming engine and  may be inaccurately classified as background noise. In contrast, if a longer audio sample was analyzed that contains more of the word (e.g., “gravi-”) were analyzed, the system may correctly classify the sound as a spoken word. Another example are words starting with a long “sss” or “sh”, which may be confused with wind sounds. Accordingly, by not considering a continuation of the sound, one or more words may be missed and not transmitted to the user. Therefore, using conventional techniques, decreasing the latency of transmission of audio signals may impact the user experience in other ways. 
     To address these and other issues associated with transmission of audio to a hearing interface device, the disclosed systems may continue to analyze a sound at longer sample periods after an initial classification of a sound has been made. If the classification result based on the longer sample period indicates that a sound is indeed a word (or another sound that should be transmitted), the word or sound may be transmitted retroactively, for example, from a buffer storing preceding samples. Accordingly, this retroactive analysis of longer samples may allow for more accurate classification of sounds, even at the cost of lower latency of transmission (i.e., with minimal delay). In some embodiments, the disclosed embodiments may further include transmitting a sound that is later classified as a desired sound at a faster rate in order to avoid an accumulated latency. For example, this may include increasing a transmission rate of one or more portions of a word or other sound being transmitted. To further reduce the delay, if required, a pause following the word and preceding the next word may be reduced Accordingly, in view of these and other aspects that will be recognized by one skilled in the art, the disclosed embodiments provide at least improved efficiency and performance over conventional techniques. 
     In some embodiments, a series of processing engines or other operators may be implemented to analyze sounds at sample periods which may vary in length to determine whether the sounds should be transmitted. For example, a first processing engine may analyze un audio signal after a first delay, a second processing engine may analyze the audio signal at a second delay, and so on.  FIG. 26  illustrates example processing engines that may be used to analyze an audio signal, consistent with the disclosed embodiments. As illustrated in  FIG. 26 , the disclosed systems may include a first processing engine  2610 , a second processing engine  2620 , and a third processing engine  2630 . Each processing engine may be operated to analyze different sample periods of an audio signal to determine whether a portion of the audio signal should be transmitted, as described in further detail below. While three processing engines are shown by way of example in  FIG. 26 , the disclosed embodiments may include any suitable number of processing engines, including two, three, four, five, or the like.  
     The various processing engines may be implemented in various ways. As used herein, a processing engine may refer to any set of instructions that, when executed, cause a processor to perform a specified processing operation. In some embodiments, processing engines  2610 ,  2620 , and  2630  may each be associated with distinct processing devices or groups of processing devices. For example, processing engine  2610  may include a set of instructions carried out by one or more processors dedicated to processing engine  2610 . In some embodiments, however, the same processor or group of processors may perform operations associated with two or more of processing engines  2610 ,  2620 , and  2630 . For example, processing engines  2610 ,  2620 , and  2630  may include sets of instructions stored in a memory device, such as memory  550  described above. A processor (or processors), such as processor  210 , may be configured to perform operations based on instructions associated with two or more of processing engines  2610 ,  2620 , and  2630 . Alternatively or additionally, each of processing engines  2610 ,  2620 , and  2630  may be associated with different memories. In some embodiments, exemplary processing engines  2610 ,  2620 , and  2630  may perform the same operations with different configurations or operation parameters. Accordingly, processing engines  2610 ,  2620 , and  2630  are not limited to any particular configuration and may be implemented using any combination of one or more memories and one or more processing devices. 
     As noted above, each of processing engines  2610 ,  2620 , and  2630  may process and/or analyze different sample periods of an audio signal.  FIG. 27  illustrates an example audio signal  2700  that may be processed using multiple processing engines, consistent with the disclosed embodiments. Audio signal  2700  may be captured by one or more microphones of wearable apparatus  110 , such as microphones  443  or  444 , as described above. In some embodiments, audio signal  2700  may be received from multiple microphones, such as a microphone array. Audio signal  2710  may include representations of sounds from the environment of a user of wearable apparatus  110 . The audio signal may therefore include voices from one or more individuals, background noise, music, and/or other sounds that may be pressed by wearable apparatus  110 . For example, the system may selectively condition audio signal  2700  to attenuate background noise, amplify sounds from a particular source (e.g., an object or person the user is looking at), adjust a pitch of the audio signal, adjust a playback rate of the audio signal, remove noise or artifacts from the signal, perform audio compression, or perform other enhancements to improve the quality of the audio for the user, as described herein. 
     As indicated above, audio signal  2700  may be processed by one or more processing engines, which may be configured to determine, based on analysis of a sample period of audio signal  2700 , whether audio signal  2700  (or at least a portion of audio signal  2700 )  should be transmitted to a hearing interface device. For example, as shown in  FIG. 27 , a first processing engine  2610  may process a first sample period  2710  of audio signal  2700 . Sample period  2710  may include any suitable portion of an audio signal for determining whether the audio signal should be transmitted. As shown in  FIG. 27 , sample period  2710  may be defined based on a time delay d 1 . In some embodiments, time delay d 1  may be a predetermined or specified time delay after a starting point in time of sample period  2710 . Alternatively or additionally, time delay d 1  may depend on other factors, such as a time required to process audio signal  2700  using processing engine  2610 , or the like. Time delay d 1  may have a duration to minimize a latency in determining whether audio signal  2700  includes speech or other desired sounds. In other words, processing engine  2610  may be configured to process a relatively short portion of audio signal  2700  to more quickly determine whether the portion of audio signal  2700  should be transmitted. 
     Based on the processing by processing engine  2710 , the system may determine whether to transmit the portion of audio signal  2700  defined by sample period  2710 . In some embodiments, processing engine  2610  may be configured to classify at least one sound represented in audio signal  2700  as a desired sound (e.g., speech, music, etc.). This may be performed using various audio analysis techniques or algorithms consistent with the embodiments described heroin. In some embodiments, the determination of whether a portion of audio signal  2700  should be transmitted using any of the various processing engines described herein may be performed using various additional information, consistent with the disclosed embodiments. For example, wearable apparatus  110  may store or access a database including voice prints from one or more individuals, as described above. Accordingly, determining whether audio signal  2700  should be transmitted may include determining whether a voice signature represented in audio signal  2700  matches a stored voice print. For example, the voice print may improve the ability of the system to recognize voices or may enable the system to distinguish between particular voices that should be transmitted from other voices that should not be transmitted. As another example, wearable apparatus  110  may include a camera, such as image sensor  220  and may detect speech based on a lip movement or other actions of individuals detected in one or more image frames that may indicate the individual is speaking, as described above. 
     If speech or other desired sounds are detected using processing engine  2610 , a portion of audio signal  2710  included in sample period  2710  may be transmitted to a hearing interface device, such as hearing interface device  1710 . Accordingly, due to the relatively short time delay d 1 , a portion of audio signal may be transmitted to hearing interface device  1710  at a low latency, thereby improving an experience for a user. In some embodiments, processing  engine  2610  may be configured to analyze audio included in sample period  2710  with minimal or no processing to minimize time delay d 1 . Alternatively or additionally processing engine  2610  may perform some degree of processing on sample period  2710 . 
     In the event processing engine  2610  docs not detect a desired sound within sample period  2710 , the system may forego transmitting audio to hearing interface device  1710 . In some embodiments, the determination not to transmit a portion of audio signal  2700  may be based on a confidence level at which processing engine  2610  determines audio sample  2710  includes sounds representing speech or other desired sounds. For example, processing engine  2610  may determine a confidence score indicating a level of certainty that sample period  2710  includes speech of an individual. This confidence score may be compared to a threshold confidence level to determine whether part of the audio signal should be transmitted. For example, if processing engine  2610  determines sample period  2710  includes speech with a confidence score of 60% and the system is configured to transmit portions of an audio signal when a confidence level exceeds 70%, the system may forego transmitting part of audio signal  2700  associated with sample period  2710 . While a percentage is provided by way of example, a confidence score may be represented using various other indicators, such as a number on a scale (e.g.. 0-10, 0-20, etc.), a ratio or fraction, a text-based classifier, or any other suitable value for representing a degree of confidence. 
     In some embodiments, due at least in part to the relatively short time delay d 1 . processing device  2610  may incorrectly classify desired sounds as undesired sounds, such as background noise or other undesired sounds. For example, in the example shown in  FIG. 27 , audio signal  2700  may include a representation of a voice of an individual speaking the word “gravity.” Sample period  2710  may include a relatively small portion of the full word, which may lead to inaccuracies in how sounds represented in sample period  2710  are classified. In the example shown in  FIG. 27 , sample period  2710  may include a first part of the word “gravity,” which may be improperly classified as background noise (or classified as speech with a relatively low degree of confidence). For example, the sound “gr-” may sound similar to a hum of machinery, an automobile running, generic background noise, or the like and may be improperly classified as such. 
     As shown in  FIG. 27 , a second processing engine  2620  may then analyze a second sample period  2720  of audio signal  2700 . Sample period  2720  may include a second portion of audio signal  2700  that includes at least part of the portion of audio signal  2700  defined by sample period  2710 . Sample period  2720  may have a duration such that second processing engine  2620  analyzes audio signal  2700  after a second delay d 2 , which may be longer than time delay d 1 . Accordingly, sample period  2720  may be a continuation of sample period  2710 .  While  FIG. 27  illustrates sample periods  2710  and  2720  beginning at the same point in time, this may not necessarily be so. For example, sample period  2720  may begin before or after sample period  2710  such that sample period  2720  includes at least a portion of sample period  2710 . Similar to processing engine  2610 , processing engine  2620  may determine whether audio signal  2700  includes speech or other desired audio based on analysis of sample period  2720 . 
     Due to the longer delay d 2  (and therefore u longer sample of audio signal  2700  to analyze), processing engine  2620  may determine with a greater degree of accuracy whether audio signal  2700  includes speech. For example, sample period  2720  may include a longer portion of a spoken word as compared to sample period  2710 . In the example shown in  FIG. 27 . this may include a portion “gravi-” of a spoken word “gravity.” Accordingly, the sounds in sample period  2720  may be more easily recognized as speech than the sounds in audio signal  2710 . Based on processing engine  2620 , the system may thus classify audio signal  2700  as including speech (or at least classify audio signal  2700  as including speech with a greater degree of confidence relative to a degree of confidence associated with processing engine  2710 ) and may transmit a portion of audio signal  2700  to hearing interface device  1710 . In some embodiments, this may include retroactively transmitting at least a portion of audio signal  2700  previously determined not to be transmitted using processing engine  2610 . In other words, rather than just transmitting “-avi-” or some fragment of the full word, audio may be transmitted beginning at a start point of a desired sound, such as the beginning of a word. In some embodiments, processing engine  2620  may analyze sample period  2720  with minimal or no processing. Alternatively or additionally, processing engine may process audio within sample period  2720 . For example, the longer delay d 2  may cause greater processing time than delay d 1 . In some embodiments, the processing of sample period  2720  may allow for more accurate or improved audio classification and/or processing. For example, this may include attenuating a background noise or a voice associated with at least one speaker, or any of the various forms of conditioning described herein. 
     This same process may be repeated using any number of processing engines. For example, if processing of sample period  2720  using processing engine  2620  results in a determination that audio signal  2700  should not be transmitted, a third processing engine  2630  may analyze a third sample period  2730 . Similar to sample period  2620 , sample period  2630  may include all or parts of sample periods  2710  and  2720  and therefore may represent a greater portion of audio signal  2700 . Based on sample period  2630 , the system may determine whether a portion of audio signal  2700  should be transmitted. This may include analyzing sample period  2630  with minimal or no additional processing, or may include various forms of conditioning of audio signal  2700 , as described above. While three sample periods  2710 ,  2720 , and  2730  are  shown in  FIG. 27 , any number of sample periods and processing engines may be used. For example, based on a determination using processing engine  2630  that audio signal  2700  should not be transmitted, one or more additional processing engines may analyze additional sample periods (not shown in  FIG. 27 ). Accordingly, the system may be configured to retroactively analyze an audio signal after multiple different delay periods to determine whether the audio signal should be transmitted. 
     In some embodiments, a portion of audio signal  2700  may be transmitted at an increased rate relative to an original rate of audio signal  2700  (e.g., a rate at which audio signal  2700  is captured, etc.). For example, if the system determines based on processing engine  2630  that sample period  2730  includes a spoken word, the full word (e.g.. “gravity”) may be transmitted retroactively, as described above. To account for the increased delay introduced based on the difference between delay d 3  and delay d 1 , the transmitted portion of audio signal  2700  may be at an increased rate relative to the original rate. This increased rate may occur in various ways. In some embodiments, this may include playing some or all of the transmitted portion of audio signal  2700  at an increased rate. In some embodiments, some or all of the portion of the word that was not initially identified as a spoken word may be omitted or condensed. Alternatively or additionally, the entire word may be condensed. This may further Include adjusting a pitch of the transmitted portion to maintain a consistent pitch relative to the original audio signal. In some embodiments, the increased rate may be achieved by reducing a spacing between words represented in the transmitted audio signal. For example, to account for the increased delay, a spacing between five word “gravity” and one or more succeeding words represented in audio signal  2700  may be reduced, which may be less noticeable to a user of the hearing aid system. Accordingly, the disclosed embodiments may allow for retroactive transmission of audio at an increased rate. 
       FIG. 28  is a flowchart showing an example process  2800  for selectively transmitting audio signals, consistent with the disclosed embodiments. Process  2800  may be performed by at least one processing device of a wearable apparatus, such as processor  210 . It is to be understood that throughout the present disclosure, the term “processor” is used as a shorthand for “at least one processor.” In other words, a processor may include one or more structures that perform logic operations whether such structures are collocated, connected, or dispersed. In some embodiments, a non-transitory computer readable medium may contain instructions that when executed by a processor cause the processor to perform process  2800 . Further, process  2800  is not necessarily limited to the steps shown in  FIG. 28 , and any steps or processes of the various embodiments described throughout the present disclosure may also be included in process  2800 , including those described above with respect to  FIGS. 26 and 27 .  
     In step  2810 , process  2800  may include receiving an audio signal representative of sounds captured by a microphone from an environment of a user of a hearing aid system. For example, microphones  443  or  444  (or microphones  1720 ) of apparatus  110  may capture sounds from the environment of the user and may transmit them to processor  210 . This may include receiving audio signal  2700  as described above. In some embodiments, the audio signal may be associated with an original rate, which may correspond to the rate at which the audio is recorded or captured, or any other reference rate. 
     In step  2820 , process  2800  may include processing a first sample period of the audio signal using a first engine. For example, this may include processing sample period  2710  of audio signal  2700  using processing engine  2610 . In some embodiments, the first sample period may include a first portion of the audio signal starting at a first point in time of the audio signal, as described above. In some embodiments, the first engine may be configured to process the audio signal to minimize a latency associated with transmitting audio to a hearing interface device. For example, processing the first sample period of the audio signal using the first engine may not include altering the audio signal. Alternatively or additionally, processing the first sample period of the audio signal may include some degree of processing, including the various forms of selective conditioning described herein. 
     In step  2822 , process  2800  may include determining, based on the processing of the first sample period of the audio signal using the first engine, whether the audio signal is to be transmitted to a hearing interface device. Based on a determination that the audio signal is to be transmitted to a hearing interface device, process  2800  may proceed to step  2840 . Conversely, based on a determination that the audio signal is not to be transmitted to the hearing interface device, process  2800  may proceed to step  2830 , as shown. The determination whether the audio signal is to be transmitted to a hearing interface device may be made in various ways. In some embodiments, determining that the audio signal is not to be transmitted to the hearing interface device based on the processing of the first sample period may include determining whether the audio signal includes background noise. For example, a determination that the audio signal is not to be transmitted to the hearing interface device may be based on the first engine determining that the first sample period docs not indicate the audio signal includes a spoken word (either correctly or incorrectly). In the example shown in  FIG. 27 , this may include determining that a portion of a word, “gr-,” does not include a spoken word and instead corresponds to background noise. As another example, the determination that the audio signal is not to be transmitted to the hearing interlace device is based on the first engine determining that the first sample period includes a spoken word at a first confidence level that does not satisfy a threshold, as described above. The determination may be performed, for example, by providing the audio signal to a  trained engine, such as a neural network, as detailed above and receiving an indication such as a probability that the audio signal is a spoken word. In some embodiments, the engine may provide an indication such as a probability that the audio signal is a background noise. 
     In step  2830 , process  2800  may include processing a second sample period of the audio signal using a second engine. As indicated by step  2822 , step  2830  may be performed alter determining that the audio signal is not to be transmitted to the hearing interface device based on the processing of the first sample period of the audio signal using the first engine. As described above, this may include processing sample period  2620  of audio signal  2700  using processing engine  2720 . Accordingly, the second sample period of the audio signal may include a second portion of the audio signal, which may include at least part of the first portion of the audio signal. The second portion of the audio signal may end at a second point in time of the audio signal, which may be at a time delay after the first point in time, as shown in  FIG. 27 . The second portion of the audio signal may start at the first point in time or a time period after the first point in time. In some embodiments, processing the second sample period of live audio signal using the second engine may include attenuating or eliminating at least one of a background noise or a voice associated with at least one speaker, as described above. For example, this may include separating the background noise or the voice of the at least one speaker from a voice associated with at least one additional speaker. 
     In step  2832 , process  2800  may include determining, based on the processing of the second sample period of the audio signal using the second engine, whether the audio signal is lo be transmitted to the hearing interface device. Similar to step  2822 , based on a determination that the audio signal is to be transmitted to a hearing interface device, process  2800  may proceed to step  2840 . Conversely, based on a determination that the audio signal is not to be transmitted to the hearing interface device, process  2800  may continue to conclude that audio signal is not to be transmitted, or may continue processing the audio signal with additional engines, as described further below. Whether process  2800  determines that audio signal is to be transmitted or not, the audio signal may be captured and analyzed in an ongoing continuous manner, such that parts thereof may be transmitted and parts thereof may not be transmitted. 
     In some embodiments, a determination that the audio signal is to be transmitted to the hearing interface device may be based on the second engine determining that the audio signal includes a spoken word (or includes a spoken word at a confidence level that satisfies a threshold). In some embodiments, the determination that the audio signal is to be transmitted based on the processing of the second sample period may be based at least on analysis of at least one image captured by the image capture device. For example, process  2800  may further include receiving images captured from the environment of the user and step  2832  may include  analyzing the images to determine whether the audio signal includes a voice of a user. In some embodiments, the analysis of the at least one image may include tracking a lip movement associated with at least one speaker represented in the at least one image, as described above. 
     In step  2840 , process  2800  may include transmitting at least a part of the first portion of the audio signal to the hearing interface device. As described above, step  2840  may be performed after determining that the audio signal is to be transmitted to the hearing interface device based on the processing of the first sample period of the audio signal using the first engine, or the processing of the second sample period of the audio signal using the second engine. In some embodiments, the at least a part of the first portion of the audio signal may be transmitted to the hearing interface device at an increased rate, which may be faster than live original rate. Transmitting at least the first portion of the audio signal to the hearing interface device at the increased rate may at least partially compensate for the time delay as described above. The increased rate may be achieved in various ways. For example, transmitting at least the part of the first portion of the audio signal to the hearing interface device at the increased rate may include reducing a spacing between a first word and a second word represented in the first portion of the audio signal. As another example, transmitting at least the part of the first portion of the audio signal to the hearing interface device at the increased rate may include increasing a transmission rate of a word or pan thereof represented in the part of the first portion of the audio signal. In some embodiments, increasing the transmission rate may include omitting a portion of the word. It is appreciated that the audio signal may be further continuously captured, analyzed and transmitted to the hearing interface device. 
     In some embodiments, process  2800  may include additional steps not shown in  FIG. 28 . For example, the process described above may be repeated with additional processing engines and sample periods, as described above. Accordingly, if step  2832  results in a determination that the audio signal should not be transmitted, process  2800  may include processing a third sample period of the audio signal using a third engine. For example, this may include processing sample period  2730  using processing engine  2630 , as described above. The third sample period of the audio signal may include a third portion of the audio signal, the third portion of the audio signal including at least part of the second portion of the audio signal or at least part of the first portion of the audio signal. The third portion of the audio signal may end at a third point in time of the audio signal, the third point in time being at a time delay after the second point in time. Process  2800  may further include determining, based on the processing of the third sample period of the audio signal using the third engine, whether the audio signal is to be transmitted to the hearing interface device (similar to steps  2822  and  2832 ). Based on a determination that the audio signal is to be transmitted to the hearing interface device based on  the processing of the third sample period of the audio signal using the third engine, process  2800  may proceed to step  2840  to transmit at least a part of the first portion of the audio signal to the hearing interlace device at an increased rate, the increased rate being faster than the original rate. Based on a determination that five audio signal is not to be transmitted to the hearing interface device based on the processing of the third sample period of the audio signal using the third engine, process  2800  may include either concluding that the audio signal is not to be transmitted or processing the audio signal using additional engines. Whether process  2830  determines that audio signal is to be transmitted or not, the audio signal may be captured and analyzed in an ongoing continuous manner, such that parts thereof may be transmitted and parts thereof may not be transmitted. 
     The foregoing description has been presented for purposes of illustration. It is not exhaustive and is not limited to the precise forms or embodiments disclosed. Modifications and adaptations will be apparent to those skilled in the an from consideration of the specification and practice of the disclosed embodiments. Additionally, although aspects of the disclosed embodiments are described as being stored in memory, one skilled in the ail will appreciate that these aspects can also be stored on other types of computer readable media, such as secondary storage devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB media, DVD, Blu-ray, Ultra HD Blu-ray, or other optical drive media. 
     Computer programs based on the written description and disclosed methods are within the skill of an experienced developer. The various programs or program modules can be created using any of the techniques known to one skilled in the art or can be designed in connection with existing software. For example, program sections or program modules can be designed in or by means of .Net Framework, .Net Compact Framework (and related languages, such as Visual Basic, C. etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with included Java applets. 
     Moreover, while illustrative embodiments have been described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of aspects across various embodiments), adaptations and/or alterations as would be appreciated by those skilled in the art based on the present disclosure. The limitations in the claims are to be interpreted broadly based on the language employed in the claims and not limited to examples described in the present specification or during the prosecution of the application. The examples are to be construed as non-exclusive. Furthermore, the steps of the disclosed methods may be modified in any manner, including by reordering steps and/or inserting or deleting steps. It is intended, therefore, that five specification and examples be  considered as illustrative only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.