Patent Publication Number: US-2020296510-A1

Title: Intelligent information capturing in sound devices

Description:
BACKGROUND 
     Noise reduction is a process of removing or reducing background noise from a sound signal such that a desired sound can be more noticeable. For example, a desired sound may be a conversation with another person or music played via a speaker or headphone. The desired sound, however, can sometimes be obscured or even rendered inaudible due to background noises. Examples of background noises can include sounds from traffic, alarms, power tools, air conditioning, or other sound sources. By reducing or removing background noises, a desired sound can be more readily detected, especially by people who are hearing impaired. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     Various techniques have been developed to reduce or remove background noises from a sound signal. For example, certain hearing aids can detect and remove background noises at certain frequencies via spectral extraction, non-linear processing, finite impulse response filtering, or other suitable techniques. By applying such techniques, background noises can be suppressed or attenuated to emphasize human speech. In another example, a noise canceling headphone can detect ambient noises (e.g., sounds from refrigerators, fans, etc.) from outside the headphone using one or more microphones. The detected ambient noises can then be removed or suppressed by applying corresponding sound waves with opposite amplitudes. As such, music, conversations, or other suitable sound played through the headphone can be heard without interference from the ambient noises. 
     The foregoing techniques for attenuating background noises, however, have certain drawbacks. For instance, removing background noises from a detected sound single may also remove important information contained in the background noises. In one example, background noises from a detected sound signal may contain sounds of an alarm, a door knock, an emergency siren, an approaching vehicle, etc. In another example, a person wearing a noise canceling headphone may not notice someone is calling his/her name or is shouting out a warning about on-coming traffic or other dangers. As such, removing background noises can render a person less aware of his/her environment, and thus negatively impact his/her safety, interactions with other people, or other aspects of the person&#39;s daily life. 
     Several embodiments of the disclosed technology can address at lease certain aspects of the foregoing drawbacks by implementing intelligent information capturing in a sound device. In one embodiment, a sound device can be a hearing aid suitable for improving hearing ability of a person with hearing impairment. In other embodiments, the sound device can also include a noise canceling headphone, a noise isolating headphone, or other suitable types of listening device. In some embodiments, the sound device can include one or more microphones, one or more speakers, a processor, and a memory containing data representing a set of sound models. The processor of the sound device can be configured to execute instructions to perform intelligent information capturing based on the sound models, as described in more detail below. 
     In certain embodiments, the microphones of the sound device can be configured to capture a sound signal from an environment in which the sound device is located. The captured sound signal is referred to herein as an original sound and can have a frequency range, such as from about 100 Hz to about 8000 Hz, from about 600 Hz to about 1600 Hz, or other suitable values. In certain implementations, the original sound can be divided into a number of frequency bands, for instance, ten to fifteen frequency bands from about 100 Hz to about 8000 Hz. The original sound can then be digitized, for instance, by converting an analog signal from the microphones at each frequency band (or in other suitable manners) into a digital signal (referred to herein as a “digitized signal”) using an analog-to-digital converter (ADC). The digitized signal can then be compared with one or more sound models stored at the memory of the sound device or otherwise accessible by the sound device via, for instance, a computer network such as the Internet. 
     The sound models can individually include an identification of a sound, one or more corresponding sound signature(s) of the sound, and one or more corresponding actions. For instance, one example sound model can identify a known sound of an approaching vehicle. Another example sound model can identify a sound of an emergency siren or an alarm. A further example sound model can identify human speech. Example sound signatures can include values, value ranges, or patterns of frequency, frequency distribution, sound amplitude at frequency bands, frequency/amplitude variations (e.g., repetitions, attenuations, etc.), and/or other suitable parameters of the corresponding sound. 
     The sound signatures can be developed according to various suitable techniques. In certain implementations, a model developer can be configured to develop the sound signatures from a training dataset. For instance, a sample sound (e.g., a sound from an approaching vehicle) can be captured using one or more microphones and then digitized using an ADC into a training dataset. According to one example technique, the model developer can then treat frequency spectra of the training dataset as vectors in a high-dimensional frequency feature domain. In such a domain, a vector distribution, e.g., a mean frequency vector of the training dataset can be calculated and then subtracted from each vector in the training dataset. To capture variation of the frequency vectors within the training dataset, eigenvectors of the covariance matrix of a zero-mean-adjusted training dataset can be calculated. The eigenvectors can represent principal components of the vector distribution. For each eigenvector, a corresponding eigenvalue indicates an importance level of the eigenvector in capturing the vector distribution. Thus, for each training dataset, a mean vector and corresponding most important eigenvectors together can represent a sound signature of the sound of the approaching vehicle. 
     During operation, when a new sound (not in the training dataset) is detected, the processor of the sound device can be configured to compare a spectrum vector of the captured new sound against the mean vector of the sound model. A difference vector can then be projected into principal component directions to find a residual vector. The coefficients of the residual vector can then be used to identify whether the new sound is a sound from a vehicle as represented in the training dataset. For example, a magnitude of the residual vector can measure the extent to which the captured new sound deviates from that in the sound model. In certain embodiments, if the magnitude of the residual vector is below a preset threshold, the sound device can indicate that the captured new sound matches that in the training dataset. In other embodiments, the captured new sound can be deemed matching the sound in the training dataset based on other suitable criteria. 
     In other implementations, the model developer can be configured to identify sound signatures based on training datasets using a “neural network” or “artificial neural network” configured to “learn” or progressively improve performance of tasks by studying known examples. In certain implementations, a neural network can include multiple layers of objects generally refers to as “neurons” or “artificial neurons.” Each neuron can be configured to perform a function, such as a non-linear activation function, based on one or more inputs via corresponding connections. Artificial neurons and connections typically have a contribution value that adjusts as learning proceeds. The contribution value increases or decreases a strength of an input at a connection. Typically, artificial neurons are organized in layers. Different layers may perform different kinds of transformations on respective inputs. Signals typically travel from an input layer, to an output layer, possibly after traversing one or more intermediate layers. Thus, by using a neural network, the model developer can provide a set of sound models that can be used by the sound device to recognize certain sounds (e.g., approaching vehicles, human speech, etc.) in the captured sound signal. In additional implementations, the model developer can be configured to perform sound signature identification based on user provided rules or via other suitable techniques. 
     In any of the foregoing implementations, upon identifying the digitized signal of the captured sound signal matches at least one sound model, the sound device can be configured to perform one or more corresponding actions included in the sound model. For instance, the sound device can be configured to determine whether the captured sound signal represents and/or includes human speech. In certain embodiments, upon determining that the detect sound includes human speech, the sound device can be configured to playback the human speech directly to a user of the sound device via the one or more speakers. In other embodiments, upon determining that the captured sound signal includes human speech, the sound device can be configured to extract the human speech (e.g., via spectral extraction and/or signal to noise enhancement) and perform speech to text conversion to derive a speech text via, for instance, feature extraction or other suitable techniques. 
     Based on the derived speech text, the sound device can be configured to perform various additional actions indicated in the corresponding sound model. For example, the sound device can be configured to determine whether the speech text represents a command from the user of the sound device. For instance, the speech text can include a command such as “up volume” or “lower volume.” In response, the sound device can be configured to incrementally or in other suitably manners increase a volume setting on the speakers of the sound device. In another example, the sound device may be operatively coupled to a computing device (e.g., a smartphone), and the speech text can include a command for interacting with the computing device, such as “call home.” In further examples, the sound device and/or the computing device can be communicatively coupled to a digital assistant, such as Alexa provided by Amazon.com of Seattle, Washington. The command can include a command that interacts with the digital assistant. For instance, the command can cause the digital assistant to perform certain operations, such as creating a calendar item, send an email, turning on a light, etc. 
     In yet further examples, the sound device can be configured to determine whether the speech text includes one or more keywords preidentified by the user and perform a corresponding preset operation accordingly. For example, a keyword can be selected by the user to include the user&#39;s name (e.g., “Bob”). Upon determining that the speech text represents someone calling “Bob,” in one embodiment, the sound device can be configured to playback a preconfigured message to the user via the speakers of the sound device, such as “someone just called your name.” In another instance, the sound device can also provide a text, sound, or other suitable forms of notification on a connected device, such as a smartphone, in addition to or in lieu of performing playback of the preconfigured message. 
     In response to determining that the captured sound signal does not include human speech, the sound device can be configured to identify one or more known sounds (e.g., a sound of an approaching vehicle) from the digitized signal based on the sound models. Upon identifying one or more known sounds, the sound device can be configured to select for playback a preconfigured message corresponding to the detected known sounds. For example, upon determining that the identified sound is that of an approaching vehicle, the sound device can be configured to select a preconfigured message such as “warning, vehicle approaching.” In one embodiment, the sound device can then be configured to perform text to speech conversion of the selected preconfigured message and then playback the message to the user via the speakers of the sound device. In other embodiments, the sound device can also be configured to provide a text, a sound, a flashing light, or other suitable forms of notification on a connected device (e.g., a smartphone) in addition to or in lieu of playback the selected message. 
     Several embodiments of the disclosed technology can thus improve the user&#39;s awareness of his/her environment by capturing useful information that is normally discarded when suppressing background noises. For example, by identifying a sound of a vehicle approaching, an emergency siren, or other alarms from background noises, the sound device can promptly provide notifications to the user via the speakers of the sound device and/or a connected smartphone. As such, safety of the person can be improved. In another example, by identifying a captured sound signal includes a door knock or someone calling the user&#39;s name, interaction and attentiveness of the user can also be improved. 
     In the foregoing description, various operations of intelligent information capturing are described as being performed by the processor of the sound device. In other implementations, at least some of the foregoing operations of intelligent information capturing can be performed by a computing device (e.g., a smartphone) operatively coupled to the sound device via, for instance, a Bluetooth, WIFI, or other suitable connection. As such, the set of sound models can be stored in the computing device instead of the sound device. In further implementations, the sound device and/or the computing device can be communicatively connected to a remote server (e.g., a server in a cloud computing data center), and at least some of the operations of intelligent information capturing, such as identifying sound(s) based on sound models, can be performed by a virtual machine, a container, or other suitable components of the remote server. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A-1E  are schematic diagrams illustrating a sound device implementing intelligent information capturing during certain stages of operation in accordance with embodiments of the disclosed technology. 
         FIGS. 2A and 2B  are schematic diagrams illustrating a sound device operatively coupled to a mobile device implementing intelligent information capturing during certain stages of operation in accordance with embodiments of the disclosed technology. 
         FIG. 3A and 3B  are schematic diagrams illustrating a sound device operatively coupled to a remote server implementing intelligent information capturing during certain stages of operation in accordance with embodiments of the disclosed technology. 
         FIG. 4  is a schematic diagram illustrating a model developer configured to develop sound models in accordance with embodiments of the disclosed technology. 
         FIG. 5  is a schematic diagram illustrating an example schema for a sound model in accordance with embodiments of the disclosed technology. 
         FIGS. 6A and 6B  are flowcharts illustrating processes of intelligent information capturing in sound devices in accordance with embodiments of the disclosed technology. 
         FIG. 7  is a computing device suitable for certain components of the computing system in  FIGS. 1A-3B . 
     
    
    
     DETAILED DESCRIPTION 
     Certain embodiments of systems, devices, components, modules, routines, data structures, and processes for intelligent information capturing in sound devices are described below. In the following description, specific details of components are included to provide a thorough understanding of certain embodiments of the disclosed technology. A person skilled in the relevant art will also understand that the technology can have additional embodiments. The technology can also be practiced without several of the details of the embodiments described below with reference to  FIGS. 1A-7 . 
     As used herein, “sound” generally refers to a vibration that can propagate as a wave of pressure through a transmission medium such as a gas (e.g., air), liquid (e.g., water), or solid (e.g., wood). A sound can be captured using an acoustic/electric device such as a microphone to convert the sound into an electrical signal. In certain implementations, the electronical signal can be an analog sound signal. In other implementations, the electrical signal can be a digital sound signal by, for example, sampling the analog sound signal using an ADC. A sound can be produced using an electroacoustic transducer, such as a speaker that converts an electrical signal into a corresponding sound. 
     Also used herein, an “ambient sound” generally refers to a composite sound that can be captured by a microphone or heard by a person in an environment in which the microphone or person resides. Ambient sound can include both desired sound, such as a conversation with another person or music played in a speaker or headphone, and unwanted sound referred to herein as “noise,” “background noise,” or “ambient noise.” Examples of noises can include sounds from traffic, alarms, power tools, air conditioning, or other sound sources. 
     Noises in an ambient sound can sometimes obscure or even render inaudible desired sound, such as a desired conversation or music. Various techniques have been developed to reduce or remove background noises from a sound signal. For example, certain hearing aids can detect and remove background noises at certain frequencies via spectral extraction, non-linear processing, finite impulse response filtering, or other suitable techniques. By applying such techniques, background noises can be suppressed or attenuated to emphasize desired human speech. In another example, a noise canceling headphone can detect ambient noises (e.g., sounds from refrigerators, fans, etc.) from outside the headphone using one or more microphones. The detected ambient noises can then be removed or suppressed by applying corresponding sound waves with opposite amplitudes. As such, desired music, conversations, or other suitable sound played through the headphone can be heard without interference from the ambient noises. 
     The foregoing techniques for attenuating background noises, however, have certain drawbacks. For instance, removing background noises from an ambient sound may also remove important information contained in the background noises. In one example, the background noises may contain sounds of an alarm, a door knock, an emergency siren, an approaching vehicle, etc. In another example, a person wearing a noise canceling headphone may not notice someone is calling his/her name or is shouting out a warning about on-coming traffic or other dangers. As such, removing background noises can render a person less aware of his/her environment, and thus negatively impact his/her safety, interactions with other people, or other aspects of the person&#39;s daily life. 
     Several embodiments of the disclosed technology can address at lease certain aspects of the foregoing drawbacks by implementing intelligent information capturing in a sound device, such as a hearing aid or headphone. In certain embodiments, an ambient sound can be captured using a microphone. The ambient sound can then be digitized into a digital sound signal. A sound device can then analyze the digital sound signal to determine whether the digital sound signal contains one or more signal profiles that match sound signatures in one or more sound models. In response to determining that the digital sound signal has a sound profile that matches the sound signature of one of the sound models, the sound device can output, via the speaker, an audio message to the user identifying the known sound while suppressing the captured ambient sound from the environment. As such, ambient noises can be suppressed while useful information from the suppressed ambient noises can be maintained, as described in more detail below with reference to  FIGS. 1A-7 . 
       FIGS. 1A-1E  are schematic diagrams illustrating a sound device  102  implementing intelligent information capturing during certain stages of operation in accordance with embodiments of the disclosed technology. Not all components are shown in every figure herein for clarity. In  FIGS. 1A-1E  and in other Figures herein, individual software components, objects, classes, modules, and routines may be a computer program, procedure, or process written as source code in C, C++, C#, Java, and/or other suitable programming languages. A component may include, without limitation, one or more modules, objects, classes, routines, properties, processes, threads, executables, libraries, or other components. Components may be in source or binary form. Components may include aspects of source code before compilation (e.g., classes, properties, procedures, routines), compiled binary units (e.g., libraries, executables), or artifacts instantiated and used at runtime (e.g., objects, processes, threads). 
     Components within a system may take different forms within the system. As one example, a system comprising a first component, a second component and a third component can, without limitation, encompass a system that has the first component being a property in source code, the second component being a binary compiled library, and the third component being a thread created at runtime. The computer program, procedure, or process may be compiled into object, intermediate, or machine code and presented for execution by one or more processors of a personal computer, a network server, a laptop computer, a smartphone, and/or other suitable computing devices. 
     Equally, components may include hardware circuitry. A person of ordinary skill in the art would recognize that hardware may be considered fossilized software, and software may be considered liquefied hardware. As just one example, software instructions in a component may be burned to a Programmable Logic Array circuit or may be designed as a hardware circuit with appropriate integrated circuits. Equally, hardware may be emulated by software. Various implementations of source, intermediate, and/or object code and associated data may be stored in a computer memory that includes read-only memory, random-access memory, magnetic disk storage media, optical storage media, flash memory devices, and/or other suitable computer readable storage media excluding propagated signals. 
     As shown in  FIG. 1A , a user  101  can wear, carry, or otherwise have a sound device  102  in an environment  100  with an ambient sound  122 . In the illustrated example, the ambient sound  122  includes a siren sound from an ambulance  112 , a vehicle sound  118  of a vehicle  116 , or a conversation  120  between additional users  101 ′. In other examples, the ambient sound  122  can include sounds from power tools, machines, or other suitable sources. In the environment  100 , the ambient sound  122  can be at least partially suppressed. For example, the user  101  can be hearing impaired such that the user  101  cannot hear at least a portion of the ambient sound  122 , for instance, at certain frequency ranges. In another example, the sound device  102  can be a noise canceling and/or isolating headphone such that the sound device  102  can at least partially suppress the ambient sound  122 . In further examples, the user  101  can be at least partially isolated from the ambient sound  122  due to sound barriers or in other suitable manners. 
     In one embodiment, the sound device  102  can be a hearing aid suitable for improving hearing of the user  101  with hearing impairment. In other embodiments, the sound device  102  can also include a noise canceling headphone, a noise isolating headphone, or other suitable types of listening device. As shown in  FIG. 1A , the sound device  102  can include a processor  104 , a memory  108 , a microphone  105 , and a speaker  106  operatively coupled to one another. Though particular components of the sound device  102  is shown in  FIG. 1A , in other embodiments, the sound device  102  can also include additional and/or different hardware/software components. For example, the sound device  102  can also include additional microphones, speakers, ADCs, digital to analog converters (DACs), and/or other suitable parts. 
     The microphone  105  can be configured to capture the ambient sound  122 . The speaker  106  can be configured to produce an output sound  103  to the user  101 . In certain embodiments, the microphone  105  can be configured to capture the ambient sound  122  from the environment  100 . The captured ambient sound  122  can have a frequency range, such as from about 100 Hz to about 8000 Hz, from about 600 Hz to about 1600 Hz, or other suitable values. In certain implementations, the captured ambient sound  122  can be divided into a number of frequency bands, for instance, ten to fifteen frequency bands from about 100 Hz to about 8000 Hz. The captured ambient sound  122  can then be digitized, for instance, by converting an analog signal from the microphone  105  at each frequency band (or in other suitable manners) into a digital signal (shown in  FIG. 1A  as a “digitized signal  124 ”) using an analog-to-digital converter (ADC). The digitized signal  124  can then be compared with one or more sound models  110  stored at the memory  108  of the sound device  102 , as described below. 
     The processor  104  can include a microprocessor, a field-programmable gate array, and/or other suitable logic devices. The memory  108  can include volatile and/or nonvolatile media (e.g., ROM; RAM, magnetic disk storage media; optical storage media; flash memory devices, and/or other suitable storage media) and/or other types of computer-readable storage media configured to store data, such as records of sound models  110 , as well as instructions for, the processor  104  (e.g., instructions for performing the methods discussed below with reference to  FIGS. 6A and 6B ). The sound models  110  can individually include an identification of a known sound, one or more corresponding sound signature(s) of the sound, and one or more corresponding actions. For instance, one example sound model can identify a sound of an approaching vehicle. Another example sound model can identify a sound of an emergency siren or an alarm. A further example sound model can identify human speech. Example sound signatures can include values, value ranges, or patterns of frequency, frequency distribution, sound amplitude at frequency bands, frequency/amplitude variations (e.g., repetitions, attenuations, etc.), and/or other suitable parameters of the corresponding sound. One example data schema suitable for a sound model  110  is described in more detail below with reference to  FIG. 5 . 
     The sound signatures can be developed according to various suitable techniques. In certain implementations, a model developer  130  (shown in  FIG. 4 ) can be configured to develop the sound signatures from a training dataset. For instance, a sample sound (e.g., a sound from an approaching vehicle) can be captured using one or more microphones and then digitized using an ADC into a training dataset. According to one example technique, the model developer  130  can then treat frequency spectra of the training dataset as vectors in a high-dimensional frequency feature domain. In such a domain, a vector distribution, e.g., a mean frequency vector of the training dataset can be calculated and then subtracted from each vector in the training dataset. To capture variation of the frequency vectors within the training dataset, eigenvectors of the covariance matrix of a zero-mean-adjusted training dataset can be calculated. The eigenvectors can represent principal components of the vector distribution. For each eigenvector, a corresponding eigenvalue indicates an importance level of the eigenvector in capturing the vector distribution. Thus, for each training dataset, a mean vector and corresponding most important eigenvectors together can represent a sound signature of the sound of the approaching vehicle. In other implementations, the model developer  130  can be configured to identify sound signatures based on training datasets using a “neural network” or “artificial neural network” configured to “learn” or progressively improve performance of tasks by studying known examples, as described in more detail below with reference to  FIG. 4 . In additional implementations, the model developer can be configured to perform sound signature identification based on user provided rules or via other suitable techniques. 
     The processor  104  can be configured to execute suitable instructions to provide certain components for facilitating intelligent information capturing in the sound device  102 . For example, as shown in  FIG. 1A , the processor  104  can include an interface component  132 , an analysis component  134 , and a control component  136  operatively coupled to one another. Though particular components are shown in  FIG. 1A  for illustration purposes, in other embodiments, the processor  104  can also include a sound suppression component, a network interface component, and/or other suitable types of component. 
     The interface component  132  can be configured to receive input from the microphone  105  as well as provide an output to the speaker  106 . In one embodiment, as shown in  FIG. 1B , the interface component  132  can be configured to receive the digitized signal  124  of the captured ambient sound  122  from the microphone  105 . In other embodiments, the interface component  132  can also be configured to receive an analog signal (e.g., a 1 to 5 volt signal direct current signal, not shown) of the captured ambient sound  122  from the microphone  105  and digitize the analog signal before providing the digitized signal  124  to the analysis component  134  for further processing. 
     As shown in  FIG. 1B , the analysis component  134  can be configured to determine whether the digitized signal  124  includes a signal profile that matches the sound signature of one of the sound models  110  stored in the memory  108 . In one embodiment, the signal profile can include one or more of a range of frequency, a pattern of frequency, a range of frequency distribution, or a pattern of frequency distribution of the captured ambient sound  122 . In other embodiments, the signal profile can include other suitable parameters of the captured ambient sound  122 . For example, the analysis component  134  can be configured to compare a spectrum vector of the digitized signal  124  against the mean vector of one of the sound models  110 . A difference vector can then be projected into principal component directions to find a residual vector. The coefficients of the residual vector can then be used to identify whether the captured ambient sound is a known sound (e.g., a sound from a vehicle) indicated in the sound model  110 . For example, a magnitude of the residual vector can measure the extent to which the captured ambient sound  122  deviates from that in the sound model  110 . In certain embodiments, if the magnitude of the residual vector is below a preset threshold, the analysis component  134  can indicate that the captured ambient sound  122  matches that in the sound model  110 . In other embodiments, the captured ambient sound  122  can be deemed matching the sound in the sound model  110  based on other suitable criteria. 
     Upon identifying the digitized signal  124  of the captured ambient sound  122  matches at least one sound model  110 , the analysis component  134  can be configured to indicate such matching and provide, for example, a sound identification (shown in  FIG. 1B  as “sound ID  126 ” to the control component  136  for further processing. In turn, the control component  136  can be configured to perform one or more corresponding actions included in the sound model  110 . For instance, the sound device can be configured to determine whether the captured ambient sound  122  represents and/or includes human speech. 
     In response to determining that the captured ambient sound  122  does not include human speech, the control component  136  can be configured to identify one or more known sounds and select for playback a preconfigured message corresponding to the detected known sounds. For example, as shown in  FIG. 1C , upon determining that the identified sound is a siren  114  from an ambulance  112 , the control component  136  can be configured to instruct the speaker  106  to select a preset message  140 , such as “Watch out for ambulance.” In another example, as shown in  FIG. 1D , upon determining that the identified sound is a vehicle sound  118  of an approaching vehicle  116 , the control component  136  can be configured to instruct the speaker  106  to select a preset message  140 , such as “Vehicle approaching.” 
     In one embodiment, the control component  136  can then be configured to perform text to speech conversion of the selected preset message  140  and then playback the converted message to the user  101  via the speaker  106 . In another embodiment, the control component  136  can be configured to select a sound file (not shown) corresponding to the preset message  140  and then instruct the speaker  106  to playback the sound file. In other embodiments, the control component  136  can also be configured to provide a text, a sound, a flashing light, or other suitable forms of notification  142  (shown in  FIG. 2A ) on a connected device  111  (e.g., a smartphone shown in  FIG. 2A ) in addition to or in lieu of playback the selected message  140 . 
     In response to determining that the ambient sound  122  includes human speech, in one embodiment, the control component  136  can be configured to playback the human speech directly to the user of the sound device  102  via the speaker  106 . In other embodiments, as shown in  FIG. 1E , upon determining that the captured ambient sound  122  includes human speech, the control component  136  can be configured to extract the human speech (e.g., via spectral extraction and/or signal to noise enhancement) and perform speech to text conversion to derive a text string via, for instance, feature extraction or other suitable techniques. 
     In one implementation, the control component  136  can be configured to determine whether the text string represents a command to the sound device  102 , such as “volume up” or “volume down.” In response to determining that the text string represents a command to the sound device  102 , the control component  136  can be configured to execute the command to, for instance, adjust a volume of the speaker  106 . In another implementation, the control component  136  can be configured to determine whether the text string represents a command to a digital assistant (e.g., Alexa provided by Amazon.com of Seattle, Washington). In response to determining that the text string represents a command to a digital assistant, the control component  136  can be configured to transmit the command to the digital assistant via a computer network (not shown) and/or provide output to the user  101  upon receiving feedback from the digital assistant. In further implementations, the control component  136  can also be configured to determine whether the text string includes one or more keywords pre-identified by the user  101 . Examples of the keywords can include a name (e.g., “Bob”) of the user  101 . In response to determining that the text string includes one or more keywords pre-identified by the user  101 , the control component  136  can be configured to output an audio message to the user  101  informing the user  101  that the one or more keywords have been detected. For instance, as shown in  FIG. 1E , the control component  136  can be configured to instruct the speaker  106  to output an audio message of “Someone just called your name.” 
     In further embodiments, the control component  136  can also be configured to perform sound suppression, compensation, or other suitable operations. For example, the control component  136  can be configured to modify, an amplitude of one or more of frequency ranges of the captured ambient sound  122  and outputting the captured ambient sound  122 , via the speaker  106 , with the modified amplitude at one or more of the frequency ranges along with the preset message  140 . In another example, the control component  136  can also be configured to generate another digital or analog sound signal (not shown) having the multiple frequency ranges with corresponding amplitude opposite that of the captured ambient sound  122  and output, via the speaker  106 , the generated sound signal along with the preset message  140  to at least partially cancel or attenuate the ambient sound  122 . 
     Even though output provided to the user  101  is shown as being through the speaker  106  in  FIGS. 1A-1E , in other embodiments, the control component  136  can also be configured to provide notifications in other suitable manners. For example, as shown in  FIG. 2 , the control component  136  can also be configured to provide a notification  142  to a mobile device  111  (shown as a smartphone) of the user  101  to be displayed on the mobile device  111 . The mobile device  111  can be connected to the sound device  102  via a WIFI, Bluetooth, or other suitable types of connection. 
     In further embodiments, at least some of the operations of intelligent information capturing can be performed on the mobile device  111 . For instance, as shown in  FIG. 2B , the interface component  132  of the sound device  102  can be configured to transmit the digitalized signal  124  to the mobile device  111  via a corresponding interface component  132 ′. The analysis component  134  and the control component  136  on the mobile device  111  can then perform the foregoing operations discussed above with reference to  FIGS. 1A-1E . The mobile device  111  can the provide the preset message  140  to the sound device  102  for playback to the user  101 . 
     In yet further embodiments, at least some of the operations of intelligent information capturing can be performed on a remote server  121 , as shown in  FIG. 3A . In the illustrated embodiment, the sound device  102  is communicatively coupled to the remote server  121  (e.g., a server in a cloud computing data center) via a computer network  123  (e.g., the Internet). The interface component  132  of the sound device  102  can be configured to transmit the digitized signal  124  to the remote server  121  via the computer network  123  for processing, as described above with reference to  FIGS. 1A-1E . Subsequently, the remote server  121  can be configured to provide the preset message  140  to the sound device  102  via the computer network  123 . In yet other embodiments, the digitized signal  124  and/or the preset message  140  can be transmitted between the sound device  102  and the remote server  121  via the mobile device  111 , as shown in  FIG. 3B . 
       FIG. 4  is a schematic diagram illustrating a model developer  130  configured to develop sound models  110  in accordance with embodiments of the disclosed technology. As shown in  FIG. 4 , the model developer  130  can be configured to identify sound signatures based on training datasets  121  having captured sound  123  and corresponding sound identifications (shown in  FIG. 4  as “sound ID  126 ′) using a “neural network” or “artificial neural network” configured to “learn” or progressively improve performance of tasks by studying known examples. In certain implementations, a neural network can include multiple layers of objects generally refers to as “neurons” or “artificial neurons.” Each neuron can be configured to perform a function, such as a non-linear activation function, based on one or more inputs via corresponding connections. Artificial neurons and connections typically have a contribution value that adjusts as learning proceeds. The contribution value increases or decreases a strength of an input at a connection. Typically, artificial neurons are organized in layers. Different layers may perform different kinds of transformations on respective inputs. Signals typically travel from an input layer, to an output layer, possibly after traversing one or more intermediate layers. Thus, by using a neural network, the model developer  130  can provide a set of sound models  110  that can be used by the sound device to recognize certain sounds (e.g., approaching vehicles, human speech, etc.) in the captured sound  123 . 
       FIG. 5  is a schematic diagram illustrating an example schema  170  for a sound model in accordance with embodiments of the disclosed technology. As shown in  FIG. 5 , the example schema  170  can include a sound ID field  172 , a sound signature field  174 , one or more action fields  176  (shown as “Action  1   176 ” and “Action n  176 ′), and a preset message field  178 . The sound ID field  172  can be configured to store data representing an identification of known sound. Example identification can include a numerical code, a text description, or other suitable data. The sound signature filed  174  can be configured to store a sound signature corresponding to the sound identification. In one example, the sound signature can include a mean vector and corresponding most important eigenvectors of a sound based on spectral analysis. In other examples, the sound signature can also include other suitable parameters of the sound. The action field  176  can be configured to store data representing an operation to be performed upon detecting the sound. In one example, an action can include playback a preset message stored in the preset message field  178 . In another example, an action can include performing text to speech conversation of the preset message before playback. In further examples, the action can include amplifying the sound, attenuating the sound, or perform other suitable operations, as described above with reference to  FIGS. 1A-1E . 
       FIGS. 6A and 6B  are flowcharts illustrating processes of intelligent information capturing in a sound device  102  in accordance with embodiments of the disclosed technology. Though the processes are described below with reference to the sound device  102  and the environment  100  in  FIGS. 1A-3B , in other embodiments, the processes can also be implemented in other suitable environment. 
     As shown in  FIG. 6A , a process  200  can include detecting a sound signal of an ambient sound at stage  202 . The sound signal can be detected using, for instance, a microphone  105  in  FIG. 1A . The process  200  can then include a decision stage  204  to determine whether a signal profile of the sound signal matches a sound signature of a sound model  110  ( FIG. 1A ), as described above with reference to  FIGS. 1A-1E . In response to determining that a match is found, the process  200  can include performing certain preset operations at stage  208 . One example preset operation can include outputting, via the speaker  106  ( FIG. 1A ), an audio message to a user identifying the known sound corresponding to the sound model. Additional examples of performing preset operations are described in more detail below with reference to  FIG. 6B . The process  200  can then proceed to an optional stage of suppressing the detected sound at stage  206 . In response to determining that a match is not found, the process  200  can proceed directly to the optional stage  206 . 
     As shown in  FIG. 6B , example operations of performing preset operations can include a decision stage  220  to determine whether human speech is detected. In response to determining that human speech is detected, the operations proceed to another decision stage  221  to determine whether any predefined keywords are detected in the human speech. In response to determining that no predefined keywords are detected, or no human speech is detected, the operations return to, for instance, the optional stage  206  of  FIG. 6A . Otherwise, the operations proceed to identifying a preset message at a stage  222 . The operations can then include an optional stage  224  of performing text to speech conversion of the preset message. The operations can then proceed to outputting the preset message to the user at stage  226  and optionally outputting a notification to, for instance, a mobile device  111  ( FIG. 2A ) of the user at stage  228 . 
       FIG. 7  is a computing device  300  suitable for certain components in  FIGS. 1A-3B . For example, the computing device  300  can be suitable for the sound device  102  of  FIGS. 1A-3B , the mobile device  111  of  FIGS. 2A and 2B , or the remote server  121  of  FIGS. 3A and 3B . In a very basic configuration  302 , the computing device  300  can include one or more processors  304  and a system memory  306 . A memory bus  308  can be used for communicating between processor  304  and system memory  306 . 
     Depending on the desired configuration, the processor  304  can be of any type including but not limited to a microprocessor (μP), a microcontroller (μC), a digital signal processor (DSP), or any combination thereof. The processor  304  can include one more level of caching, such as a level-one cache  310  and a level-two cache  312 , a processor core  314 , and registers  316 . An example processor core  314  can include an arithmetic logic unit (ALU), a floating-point unit (FPU), a digital signal processing core (DSP Core), or any combination thereof. An example memory controller  318  can also be used with processor  304 , or in some implementations memory controller  318  can be an internal part of processor  304 . 
     Depending on the desired configuration, the system memory  306  can be of any type including but not limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof. The system memory  306  can include an operating system  320 , one or more applications  322 , and program data  324 . This described basic configuration  302  is illustrated in  FIG. 6  by those components within the inner dashed line. 
     The computing device  300  can have additional features or functionality, and additional interfaces to facilitate communications between basic configuration  302  and any other devices and interfaces. For example, a bus/interface controller  330  can be used to facilitate communications between the basic configuration  302  and one or more data storage devices  332  via a storage interface bus  334 . The data storage devices  332  can be removable storage devices  336 , non-removable storage devices  338 , or a combination thereof. Examples of removable storage and non-removable storage devices include magnetic disk devices such as flexible disk drives and hard-disk drives (HDD), optical disk drives such as compact disk (CD) drives or digital versatile disk (DVD) drives, solid state drives (SSD), and tape drives to name a few. Example computer storage media can include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. The term “computer readable storage media” or “computer readable storage device” excludes propagated signals and communication media. 
     The system memory  306 , removable storage devices  336 , and non-removable storage devices  338  are examples of computer readable storage media. Computer readable storage media include, but not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other media which can be used to store the desired information and which can be accessed by computing device  300 . Any such computer readable storage media can be a part of computing device  300 . The term “computer readable storage medium” excludes propagated signals and communication media. 
     The computing device  300  can also include an interface bus  340  for facilitating communication from various interface devices (e.g., output devices  342 , peripheral interfaces  344 , and communication devices  346 ) to the basic configuration  302  via bus/interface controller  330 . Example output devices  342  include a graphics processing unit  348  and an audio processing unit  350 , which can be configured to communicate to various external devices such as a display or speakers via one or more A/V ports  352 . Example peripheral interfaces  344  include a serial interface controller  354  or a parallel interface controller  356 , which can be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, scanner, etc.) via one or more I/O ports  358 . An example communication device  346  includes a network controller  360 , which can be arranged to facilitate communications with one or more other computing devices  362  over a network communication link via one or more communication ports  364 . 
     The network communication link can be one example of a communication media. Communication media can typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and can include any information delivery media. A “modulated data signal” can be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media can include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), microwave, infrared (IR) and other wireless media. The term computer readable media as used herein can include both storage media and communication media. 
     The computing device  300  can be implemented as a portion of a small-form factor portable (or mobile) electronic device such as a cell phone, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. The computing device  300  can also be implemented as a personal computer including both laptop computer and non-laptop computer configurations. 
     From the foregoing, it will be appreciated that specific embodiments of the disclosure have been described herein for purposes of illustration, but that various modifications may be made without deviating from the disclosure. In addition, many of the elements of one embodiment may be combined with other embodiments in addition to or in lieu of the elements of the other embodiments. Accordingly, the technology is not limited except as by the appended claims.