Patent Publication Number: US-11032662-B2

Title: Adjusting audio characteristics for augmented reality

Description:
I. FIELD 
     This disclosure is generally related to electronic devices and more particularly to electronic devices that adjust audio characteristics for augmented reality (AR) applications. 
     II. DESCRIPTION OF RELATED ART 
     Electronic devices may provide audio to users. For example, a user may utilize an electronic device to listen to music, conduct a telephone conversation, or play a video game. 
     In certain augmented reality (AR) applications, audio may be customized based on an environment of a user (e.g., to simulate or “augment” the environment). To illustrate, a user may wear a headset device while playing an AR game, and the headset device may present graphics that simulate or augment the environment, audio that simulates or augments the environment, or both. As a particular example, if a room has a particular acoustic characteristic (e.g., a large amount of reverberation due to a high ceiling of the room), then the audio may be modified to simulate the particular acoustic characteristic (e.g., by applying a reverb effect to the audio) so that the audio appears “real” to the user. 
     Acoustic characteristics of different environments may be determined by testing for certain environmental characteristics that differ for various environments. For example, in some applications, a test signal may be generated in an environment (e.g., a room or a theater) to determine an impulse response associated with the environment. After determining the impulse response, audio may be modified (e.g., equalized) in response to evaluating the particular impulse response. As an illustrative example, if the impulse response indicates that the environment attenuates high frequencies, then high frequencies may be attenuated in the audio to simulate the environment so that the audio appears “real” to a user. 
     In some circumstances, an impulse response based technique may be expensive or infeasible. For example, generation of a test signal and analysis of an impulse response may involve expensive equipment. As another example, an impulse response based technique may be ineffective in cases where a user rapidly changes locations (e.g., where the user moves between rooms while playing an AR game). 
     III. SUMMARY 
     In an illustrative example, an augmented reality (AR) device includes a memory configured to store instructions of an augmented reality (AR) application. The AR device further includes a processor configured to initiate a first image capture operation to generate first image data and to determine a three-dimensional (3D) map based on the first image data. The 3D map represents a set of locations including a first location. The processor is further configured to initiate a second image capture operation to generate second image data and to execute the instructions to identify, based on the second image data, a second location of the set of locations. The processor is further configured to modify an audio signal to synthesize one or more acoustic characteristics associated with audio sent from the first location to the second location. 
     In another example, a method includes performing, at an AR device, a first image capture operation to generate first image data. A 3D map representing a set of locations including a first location is determined based on the first image data. The method further includes performing a second image capture operation to generate second image data at the AR device. A second location of the set of locations is identified based on the second image data. The method further includes modifying an audio signal at the AR device to synthesize one or more acoustic characteristics associated with audio sent from the first location to the second location. 
     In another example, a computer-readable medium stores instructions executable by a processor to initiate, perform, or control operations. The operations include performing, at an AR device, a first image capture operation to generate first image data. A 3D map representing a set of locations including a first location is determined based on the first image data. The operations further include performing a second image capture operation to generate second image data at the AR device. A second location of the set of locations is identified based on the second image data. The operations further include modifying an audio signal at the AR device to synthesize one or more acoustic characteristics associated with audio sent from the first location to the second location. 
     In another example, an apparatus includes means for storing instructions of an AR application. The apparatus further includes means for executing the instructions to initiate a first image capture operation to generate first image data and to determine a 3D map based on the first image data. The 3D map represents a set of locations including a first location. The means for executing is configured to initiate a second image capture operation to generate second image data, to execute the instructions to identify, based on the second image data, a second location of the set of locations, and to modify an audio signal to synthesize one or more acoustic characteristics associated with audio sent from the first location to the second location. 
    
    
     
       IV. BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram of an illustrative example of a system that includes an augmented reality (AR) device configured to modify an audio signal based on image data. 
         FIG. 2  is diagram of certain illustrative aspects of an example of an AR device, such as the AR device of  FIG. 1 . 
         FIG. 3  is a flow diagram of an illustrative example of a method of operation of an AR device, such the AR device of  FIG. 1 . 
         FIG. 4  is a block diagram of an illustrative example of an electronic device, such as the AR device of  FIG. 1 . 
     
    
    
     V. DETAILED DESCRIPTION 
     Aspects of the disclosure are related to an augmented reality (AR) device (e.g., an AR headset) that uses image data to generate a three-dimensional map and to identify locations within the 3D map. The AR device is configured to modify an audio signal to synthesize characteristics of sound travel between the locations (e.g., in order to “simulate” the sound travel in an AR game played by a user). As an example, the AR device may artificially reverberate, mix, filter, or attenuate the audio signal to synthesize characteristics of sound from a first location (e.g., an audio source location) to a second location (e.g., a user location) so that a user perceives the audio signal as being “real.” 
     As a particular illustrative example, an AR game may provide audio to a user that simulates a particular sound, such as a knock at a door of a room. The AR device may capture an image that depicts the door and may modify an audio signal based on the image. For example, the AR device may perform an image recognition operation to detect a material (e.g., a wood material or a metal material) of the door and may modify and/or generate an audio signal based on the material, such as by modifying the audio signal to simulate knocking on wood or metal, by using a pre-recorded sample for the identified object, or by procedurally generating an audio signal to simulate an impact noise on the identified object. 
     Use of the image data to determine adjustment of an audio signal may simulate effects of one or more other techniques (e.g., an impulse response based technique) without use of certain equipment or calculations associated with the other techniques. For example, by using the image data to “match” features in an environment to certain acoustic characteristics, an AR device may avoid generation of a test signal and analysis of an impulse response that involves expensive equipment, which may be infeasible in certain AR applications, such as where the equipment cannot be integrated within a head-mounted AR headset. As another example, an impulse response based technique may be sensitive to positioning of a source of the test signal, which may be avoided using the image data instead of the test signal. As an additional example, use of the image data may enable faster tracking of a dynamic environment (or a change between environments), such as when a user moves from inside to outside a building or from one room to another room of the building, as illustrative examples. Alternatively or in addition, audio associated with movement of and/or impact on an object may be simulated, such as by simulating the sound of a door or window opening or closing during the AR game. 
     As another example, use of the image data may enable increased precision (e.g., “granularity”) of audio adjustment. For example, use of the image data may enable the AR device to separately simulate effects of a direct path from an audio source to a user location (e.g., through an object), an indirect path from the audio source to the user location (e.g., around the object), an early reflection characteristic, a late reflection characteristic, one or more other effects, or a combination thereof. As a result, audio may be adjusted more precisely as compared to certain other techniques that “globally” adjust audio parameters based on an “average” characteristic of an environment. 
     Referring to  FIG. 1 , aspects of a particular example of an augmented reality (AR) device configured to modify an audio signal based on image data are depicted and generally designated  102 . In an illustrative example, the AR device  102  may include or correspond to a wearable device, such as a headset (e.g., “goggles”) worn by a user in connection with an AR application. In other examples, the AR device  102  may include or correspond to another device, such as a handheld AR device, as an illustrative example. As used herein, “AR” is used to include AR and virtual reality (VR) applications. As used herein, an “AR device” is used to refer to a device that enables a user to engage in an AR application, such as an AR game. 
     The AR device  102  includes a memory  134  configured to store instructions  136  of an AR application. The AR device  102  further includes a processor  132  configured to execute the instructions  136 . 
     The example of  FIG. 1  also depicts that the AR device  102  includes one or more image sensors (e.g., an image sensor  108 ). In a particular example, the image sensor  108  includes a camera, such as a charge-coupled device (CCD) camera, a complementary metal-oxide-semiconductor (CMOS) camera or an n-type metal-oxide-semiconductor (NMOS) camera. Alternatively or in addition, the image sensor  108  may include one or more other sensors, such as an infrared sensor. In a particular example, the image sensor  108  includes a red, green, blue, and infrared (RGB-IR) camera. Alternatively or in addition, the AR device  102  may include one or more other sensors or devices configured to determine (or estimate) depth, size, movement, and location. For example, the AR device  102  may include a global positioning system (GPS) sensor, a gyroscopic sensor, a compass, an accelerometer, a geo-positioning sensor, a short-range radio, a radio that complies with a Bluetooth (BT) protocol or a Bluetooth Low Energy (BLE) protocol (Bluetooth is a trademark of Bluetooth Special Interest Group (SIG) of Kirkland, Wash.), a radio that complies with a Wi-Fi protocol, (Wi-Fi is a trademark of the Wi-Fi Alliance of Austin, Tex.), a sonar device, an audio fingerprinting device, one or more other sensors or devices, or a combination thereof. The image sensor  108  may include one or more image sensors (or other sensors) configured to generate data related to objects within the field of view of the user of the AR device  102 , outside the field of view of the user, or a combination thereof. 
       FIG. 1  also illustrates that the AR device  102  may include an audio sink  124 , such as one or more speakers. In a particular example, the audio sink  124  includes multiple speakers configured to generate stereo sound. The audio sink  124  may include one or more speakers configured to fit inside, outside, or around an ear of a user (e.g., an “ear bud” speaker), one or more external speakers, or a combination thereof. To further illustrate, the AR device  102  may correspond to or include a wearable device (e.g., a headset) having at least a portion configured to engage (e.g., wrap around or fit inside) the ear of the user. 
     During operation, the processor  132  is configured to initiate a first image capture operation to generate first image data  109 . To illustrate, the image sensor  108  may be configured to perform the first image capture operation to generate the first image data  109  in response to an instruction from the processor  132 . In an illustrative example, the first image capture operation is performed by the AR device  102  during a calibration process during which a user performs a “walk through” of one or more geographic areas while the image sensor  108  captures images or video to generate the first image data  109 . To illustrate, upon executing the instructions  136  (e.g., in response to user input to load an AR game or other AR application), the AR device  102  may prompt the user to walk about surroundings of the user to enable the AR device  102  to capture images or record video. 
     The AR device  102  is configured to generate and store data representing a three-dimensional (3D) map  112  based on the first image data  109 . The 3D map  112  represents a set of locations including a first location  104  (e.g., an audio source location of an audio source to be simulated), which may be included within a particular geographic area, such as a building (or a region within a building). In a particular example, the processor  132  is configured to determine the 3D map  112  by stitching together multiple images included in the first image data  109  to generate a composite image of the set of locations using a panoramic image synthesis technique. 
     In another example, data representing the 3D map  112  (or a portion of the 3D map  112 ) may be received by the AR device  102  from another device, such as a server. For example, the AR device  102  may “offload” the first image data  109  to a server to generate the 3D map  112  and may receive the 3D map  112  from the server. In some examples, the server may provide a file having a particular file format to the AR device  102 , and the file may represent the 3D map  112 . As a particular illustrative example, the file may correspond to a program (or a program installer) that is executable by the AR device and that represents the 3D map  112 . 
     The set of locations represented by the 3D map  112  further includes a second location  106 . As a non-limiting illustrating example, the first location  104  may correspond to a first room of a building or a first region within a particular room, and the second location  106  may correspond to a second room of the particular building or a second region within the particular room. 
     In an illustrative example, the 3D map  112  is generated by the AR device  102  during a calibration process during which a user performs a “walk through” of the geographic area while the image sensor  108  captures images to build the 3D map  112 . To illustrate, upon executing the instructions  136 , the AR device  102  may prompt the user to walk about surroundings of the user to enable the AR device  102  to capture images or to record video in order to generate (e.g., “build”) the 3D map  112 . In the example of  FIG. 1 , the surroundings include the first location  104  and the second location  106 . 
     To further illustrate, the 3D map  112  may indicate one or more rooms of a building that are each identified using the first image data  109 . The 3D map  112  may indicate dimensions (e.g., length, width, and height) of one or more rooms. For example, the AR device  102  may include an infrared sensor configured to determine the dimensions using an infrared sensing technique. In a particular example, the infrared sensor (or another sensor of the AR device  102 ) is configured to detect presence or location of one or more objects in a room, such as by using an infrared sensing technique to detect presence and location of a piece of furniture. 
     In some implementations, the processor  132  is configured to map features indicated by the first image data  109  to reference shapes, such as by “simplifying” or “approximating” an object as a plane, a cube, a polygon, a sphere, or a pyramid, as illustrative examples. As a particular example, a wall or a surface may be approximated as one or more planes. In this case, the 3D map  112  may depict the set of locations using reference shapes such as planes, cubes, polygons, spheres, or pyramids. 
     Alternatively or in addition, the processor  132  may be configured to identify a room represented by the 3D map  112  and to modify the audio signal  118  based on one or more acoustic characteristics that are correlated to dimensions of the room, one or more objects (e.g., a border object) of the room, wall characteristics of the room, or a combination thereof. As referred to herein, an object may correspond to an article within a room (e.g., a piece of furniture) or an article bordering a room (e.g., a door, a window, or a wall). The processor  132  may perform an image recognition operation based on the first image data  109  to detect one or more objects, such as the object  150 . As a particular illustrative example, the processor  132  may be configured to execute the instructions  136  to match an object (e.g., a window) represented by the first image data  109  to a reference object shape to detect the object. As another illustrative example, the processor  132  may be configured to execute the instructions  136  to match a door represented by the first image data  109  to a reference door shape to detect the door. The processor  132  may be configured to determine dimensions of the objects (e.g., using an infrared sensor). The 3D map  112  may indicate, for one or more rooms represented by the 3D map  112 , one or more objects. 
     The processor  132  may be configured to detect materials associated with a room or an object in order to synthesize one or more characteristics (e.g., reverberation, absorption, attenuation, or other characteristics) associated with the materials. As an example, the processor  132  may be configured to execute the instructions  136  to determine whether a door includes a wood material or a metal material. In another example the processor  132  is configure to execute the instructions  136  to determine whether a curtain is covering a window. In one example, the processor  132  is configured to execute the instructions  136  to perform an image recognition operation to match a surface indicated by the first image data  109  to a reference surface to identify a material. The 3D map  112  may indicate, for one or more rooms represented by the 3D map  112 , one or more materials, such as a surface material of an object. 
     As used herein, “synthesizing” a characteristic refers to modifying a signal to increase or create the characteristic in the signal. Synthesizing a characteristic may include operations such as simulating the characteristic in the signal, artificially reverberating the signal, performing an acoustic ray tracing operation, adding samples of a prerecorded sound (e.g., speech or other audio, which may be indicated by a template or other data), reducing one or more aspects of the signal (e.g., by filtering a particular frequency or band of frequencies), deleting a portion of the signal (e.g., by deleting a particular portion or sample of the signal), one or more other operations, or a combination thereof. As used herein, “synthesizer” refers to a device (e.g., a circuit, a processor configured to execute instructions, another device, or a combination thereof) configured to synthesize (e.g., simulate) or more characteristics in the signal. 
     The processor  132  may be configured to determine a status associated with an object in order to synthesize one or more characteristics (e.g., reverberation, absorption, attenuation, or other characteristics) associated with the object. For example, the processor  132  may be configured to execute the instructions  136  to detect a position of a door or a window, such as whether the door or window is open, closed, or ajar. Alternatively or in addition, the processor  132  may be configured to execute the instructions  136  to determine a thickness of an object or a solidity of an object (e.g., whether the object is hollow). As an example, in some implementations, the processor  132  is configured to determine (or estimate) thickness of a wall based on the width of an open door or doorway. 
     The processor  132  may be configured to determine one or more acoustic parameters associated with an object or a material. For example, the memory  134  may store an acoustic property database that indicates objects and materials and one or more acoustic parameters associated with each object or material. As a particular illustrative example, the acoustic property database may indicate a first set of acoustic properties (e.g., attenuation, absorption, reflection, and reverberation) associated with a wood door and may further indicate a second set of acoustic properties (e.g., attenuation, absorption, reflection, and reverberation) associated with a metal door. 
     The processor  132  may be configured to associate an object or material represented by the 3D map  112  with one or more acoustic properties indicated by the acoustic property database. As an example, for each detected object of the 3D map  112 , the processor  132  may generate metadata of the 3D map  112 . The metadata may indicate one or more acoustic properties associated with the corresponding object. As an example, the metadata may indicate a reflectivity of the object, an absorptivity of the object, an attenuation of the object, one or more other characteristics, or a combination thereof. In some examples, each characteristic indicated by the metadata may have a value selected from a range of values. As an example, each characteristic may be “scored” using a range of 0 to 100 (e.g., where a score of 0 indicates low or zero presence of the characteristic, and where a score of 100 indicates a high or “maximum” presence of the characteristic). 
     In a particular example, the acoustic property database indicates default (or “stock”) acoustic properties, and the processor  132  is configured to modify the default acoustic properties based on dimensions or classification of an object or room detected by the AR device  102 . As a particular illustrative example, the acoustic property database may indicate that a “stock” wooden door having a reference thickness, length, or breadth is associated with a particular acoustic characteristic. In response to detecting, using the first image data  109 , a wooden door having a thickness, length, or breadth that is different from the reference thickness, length, or breadth indicated by the acoustic property database, the processor  132  may execute the instructions  136  to modify the particular acoustic characteristic (e.g., by multiplying a coefficient corresponding to a particular filter characteristic by a factor to “adjust” or “normalize” the particular acoustic characteristic based on the dimensions of the door). Alternatively or in addition, the acoustic property database may include a template (or “profile”) associated with an object, and the processor  132  may be configured to access the template to synthesize or generate a sound associated with the object. As an illustrative example, the template may indicate acoustic properties associated with knocking at a metal or wooden door, and the processor  132  may be configured to synthesize or generate a knocking sound at a metal or wooden door based on the particular dimensions of the door and further based on the acoustic properties indicated by the template. 
     In a particular example, the processor  132  is configured to execute the instructions  136  to determine whether an amount of data generated during the calibration process satisfies a threshold to generate the 3D map  112 . For example, the processor  132  may be configured to determine whether a threshold number of images or a threshold duration of video are generated during the calibration process. Alternatively or in addition, the processor  132  may be configured to execute the instructions  136  to determine whether the images or video are sufficient to “stitch together” a composite image of the set of locations, such as using a panoramic image synthesis technique. 
     In some implementations, the processor  132  is configured to execute the instructions  136  to prompt the user to continue (or resume) the walk through in response to determining that the amount of data fails to satisfy (e.g., is less than) the threshold. The processor  132  may be configured to execute the instructions  136  to prompt the user to discontinue the walk through or to perform another action in response to determining that the amount of data satisfies (e.g., is greater than or equal to) the threshold. As a particular illustrative example, the processor  132  may be configured to execute the instructions  136  to prompt the user to enter a user mode of operation in response to detecting that the calibration process is complete based on the amount of data satisfying the threshold. 
     The processor  132  is configured to initiate a second image capture operation to generate second image data  110 . In a particular example, the image sensor  108  is configured to perform the second image capture operation to generate the second image data  110  during a user mode of operation of the AR device  102  (e.g., after generating the 3D map  112  using the “walk-through” calibration process). As used herein, a “user mode” of operation of a device may refer to a mode of operation of the device engaged in by a user after completion of a setup or calibration process, such as the “walk through” calibration process described with reference to the first image data  109  and the 3D map  112 . 
     The processor  132  is configured to execute the instructions  136  to identify, based on the second image data  110 , one or more locations of the set of locations represented by the 3D map  112 , such as the second location  106 . For example, the second location  106  may correspond to a user location that the processor  132  identifies using the second image data  110  and the 3D map  112  (e.g., by dynamically tracking the user location within the 3D map  112  using image data captured by the image sensor  108  so that acoustic characteristics of the user location can be simulated). To further illustrate, the processor  132  may perform an image recognition operation to “match” representations in the second image data  110  to features of the 3D map  112  to identify the second location  106 . As an illustrative example, the processor  132  may identify the second location  106  by “matching” features indicated in the second image data  110  to features represented by the 3D map  112  (e.g., by detecting a threshold number of similar features). 
     The processor  132  is configured to execute the instructions  136  to modify an audio signal  118  to generate a modified audio signal  119  to synthesize one or more acoustic characteristics associated with audio from the first location  104  to the second location  106  (e.g., so that audio presented to a user appears “real” to the user based on the particular surroundings or environment of the user while playing an AR game). To illustrate, the audio signal  118  may be modified to simulate an audio source  140  at the first location  104 , such as knocking at a door while a user is playing an AR game, as an illustrative example. Depending on the particular example, the audio source  140  may correspond to an actual object (e.g., a door that is at the first location) or a virtual object (e.g., a door that is to be displayed to a user in connection with an AR program executed by the AR device  102  while a user plays an AR game). 
     The audio signal  118  may be modified to simulate particular surroundings or an environment of a user based on the second image data  110 . Modifying the audio signal  118  may include one or more of augmenting a sound indicated by the audio signal  118  (e.g., by adding reverb to a knocking sound indicated by the audio signal  118 ), adding a sound to the audio signal  118  (e.g., by adding a pre-recorded or “canned” knocking sound to the audio signal  118 ), or replacing a sound indicated by the audio signal  118  (e.g., by replacing one knocking sound with another knocking sound). To further illustrate, the processor  132  may be configured to modify the audio signal  118  by equalizing the audio signal  118 , binaurally mixing the audio signal  118 , artificially reverberating the audio signal  118 , pitch shifting the audio signal  118 , resonating the audio signal  118 , changing harmonic content or formant structure of the audio signal  118 , distorting the audio signal  118 , performing one or more other operations, or a combination thereof. 
     Depending on the particular implementation, the processor  132  may be configured to modify the audio signal  118  based on a direct path  160  from the first location  104  to the second location  106 , an indirect path  170  from the first location  104  to the second location  106 , or both. To illustrate, the direct path  160  may be through one or more objects, such as the object  150 . In this example, the processor  132  may be configured to modify the audio signal  118  to simulate occlusion associated with the audio source  140  due to the one or more objects. Alternatively or in addition, the processor  132  may be configured to modify the audio signal  118  based on the indirect path  170 , such as by simulating one or more reflections from a wall or an object, by simulating diffraction of sound around one or more corners of a room or an object, or a combination thereof. The processor  132  may be configured to modify the audio signal  118  based on surroundings and a position of the first location  104  relative to the second location  106  (e.g., by simulating reverberation from an object proximate to the first location  104 , the second location  106 , or both). Examples of determination of the direct path  160  and the indirect path  170  are described further with reference to  FIG. 2 . 
     In one example, the audio signal  118  corresponds to an impact-based sound. To illustrate, the audio source  140  may correspond to a door of a room, and the audio signal  118  may correspond to an impact (e.g., knocking) at the door. The processor  132  may modify the audio signal  118  so that a user perceives audio as originating from the door (e.g., by detecting the door to include a metal material, and by modifying the sound to replicate knocking at the metal door). In this example, the audio signal  118  may include pre-recorded audio samples (e.g., pre-recorded knocking samples), procedurally generated (or recorded) audio samples, or any other type of audio sample to simulate audio from the first location  104  to the second location  106 . 
     In a particular example, the AR device  102  is configured to adjust the audio signal  118  based on a head-related transfer function (HRTF) associated with the user of the AR device  102  and based on the spatial orientation of the user. For example, the AR device  102  may be configured to binaurally mix the audio signal  118  so that the user perceives audio as originating from a particular location (e.g., in front of the user, to the side of the user, behind the user, above the user, or below the user), such as the first location  104  (instead of as originating from the AR device  102 ). 
     After modifying the audio signal  118 , the AR device  102  is configured to generate an acoustic representation of the modified audio signal  119 . For example, in  FIG. 1 , the audio sink  124  (e.g., one or more speakers) is configured to generate an acoustic representation of the modified audio signal  119 . 
     Although certain aspects of  FIG. 1  have been described with reference to the AR device  102 , it should be appreciated that certain operations may be performed by another device (alternatively or in addition to the AR device  102 ). As an example, a second device may be in communication with the AR device  102 . The second device may include a gaming console, a computer, or a security camera, as illustrative examples. The second device may include a second image sensor that captures one or more images. The second device may be provided to the AR device  102  (e.g., via a wireless network), and the one or more images may be included in the first image data  109 , as an illustrative example. 
     Use of the image data  109 ,  110  and the 3D map  112  may increase efficiency of adjusting the audio signal  118  to synthesize (e.g., simulate) characteristics of sound from the first location  104  to the second location  106 . As an example, use of the image data  109 ,  110  and the 3D map  112  may enable faster tracking of a dynamic environment (or a change between environments), such as when the environment outside the field of view of the user changes (e.g., when a door or window in another room opens or closes), which may be difficult or infeasible using other techniques. Use of the image data  109 ,  110  and the 3D map  112  may also enable more accurate and/or efficient tracking of position and/or orientation of the user within the 3D map as compared to certain other techniques. 
       FIG. 2  depicts illustrative aspects of an example of an AR device, such as the AR device  102  of  FIG. 1 .  FIG. 2  depicts that the AR device  102  includes the image sensor  108 , the processor  132 , the memory  134 , and the image-based audio modifier  138 . 
     In the example of  FIG. 2 , the image-based audio modifier  138  includes a reverb synthesizer  206 . The reverb synthesizer  206  may include a late reflection synthesizer  208  and an early reflection synthesizer  210 . The reverb synthesizer  206  may further include a binaural mixer  212  coupled to the early reflection synthesizer  210  and to the audio sink  124 . 
     In  FIG. 2 , the image-based audio modifier  138  includes a direct path calculator  220 . The image-based audio modifier  138  may include an obstacle-based filter  224  coupled to the direct path calculator  220 . The obstacle-based filter  224  may be configured to receive an output of the direct path calculator  220  and to receive the audio signal  118 . The image-based audio modifier  138  may include an obstacle-based attenuator  228  and a binaural mixer  232  that are coupled to the direct path calculator  220 . The obstacle-based attenuator  228  may be coupled to an output of the obstacle-based filter  224 , and the binaural mixer  232  may be coupled to an output of the obstacle-based attenuator  228 . The binaural mixer  232  may be coupled to the audio sink  124 . 
     Alternatively or in addition, the image-based audio modifier  138  may include an indirect path calculator  222 . The image-based audio modifier  138  may include an occlusion-based filter  226  coupled to the indirect path calculator  222 . The occlusion-based filter  226  may be configured to receive an output of the indirect path calculator  222  and to receive the audio signal  118 . The image-based audio modifier  138  may include an occlusion-based attenuator  230  and a binaural mixer  234  that are coupled to the indirect path calculator  222 . The occlusion-based attenuator  230  may be coupled to an output of the occlusion-based filter  226 , and the binaural mixer  234  may be coupled to an output of the occlusion-based attenuator  230 . The binaural mixer  234  may be coupled to the audio sink  124 . 
     The direct path calculator  220  may be configured to determine a direct path from an audio source location to a user location, such as by determining the direct path  160  of  FIG. 1 . In a particular example, the direct path  160  is through one or more obstacles that are positioned between the AR device  102  and the audio source  140 , such as the object  150 . The direct path calculator  220  may be configured to provide an indication of the direct path  160  to the obstacle-based filter  224  (e.g., by indicating coordinates of the direct path  160  within the 3D map  112 , by indicating a length of the direct path  160 , or both). In one example, the direct path calculator  220  is configured to determine the direct path  160  by determining a line segment directly joining a source location (e.g., the first location  104 ) and a listener location (e.g., the second location  106 ), which may be through one or more objects or mediums (e.g., obstacles). 
     The obstacle-based filter  224  may be configured to filter the audio signal  118  based on the direct path  160 . For example, the obstacle-based filter  224  may be configured to filter the audio signal  118  based on a frequency response (e.g., a cutoff frequency and a gain) associated with the object  150  based on a type of the object  150  and a distance of the direct path  160 . Filtering the audio signal  118  based on the direct path  160  may simulate effects of frequency selectivity of the object  150  to sound propagating through the object  150 . As an example, if the object  150  has a particular frequency selectivity characteristic (e.g., low-pass, high-pass, band-pass, or band-reject), the obstacle-based filter  224  may filter the audio signal  118  using one or more corresponding filters (e.g., a low-pass filter, a high-pass filter, a band-pass filter, a band-reject filter, or a combination thereof). 
     The obstacle-based attenuator  228  may be configured to determine an attenuation associated with the direct path  160  and to modify the audio signal  118  based on the attenuation. The obstacle-based attenuator  228  may be configured to determine the attenuation by summing a first attenuation associated with sound travel through air and a second attenuation associated with sound travel through one or more objects (e.g., the object  150 ) (e.g., due to absorptivity associated with the one or more objects). In an illustrative example, the obstacle-based attenuator  228  may “roll off” amplitude of the audio signal  118  to simulate attenuation due to absorptivity of the object  150 . In a particular example, the obstacle-based attenuator  228  is configured to determine a total amount of attenuation through each medium (e.g., objects or empty space) associated with the direct path  160  and a total amount of attenuation across one or more boundaries between the mediums. In some cases, the obstacle-based attenuator  228  may be configured to attenuate higher frequencies more than lower frequencies (e.g., to simulate different absorptive and reflective characteristics of high frequencies and low frequencies of sound). 
     The binaural mixer  232  may be configured to mix the audio signal  118  based on the direct path  160 . For example, the binaural mixer  232  may be configured to “mix” effects of the obstacle-based filter  224  and the obstacle-based attenuator  228 , such as by binaurally mixing the audio signal  118  between the left and right speakers/channels using a HRTF associated with the user in response to the direct path approaching each ear of the user at a particular azimuthal angle and polar angle (e.g., as determined by the position and orientation of the user and as calculated using the direct path  160 ). 
     Alternatively or in addition, the indirect path calculator  222  may be configured to determine an indirect path from an audio source location to a user location, such as by determining the indirect path  170  of  FIG. 1 . In a particular example, the indirect path  170  is around one or more obstacles that are positioned between the AR device  102  and the audio source  140 , such as the object  150 . In a particular example, the indirect path calculator  222  is configured to determine the indirect path  170  by identifying the shortest path (e.g., a set of line segments) between a source location (e.g., the first location  104 ) and a listener location (e.g., the second location  106 ) without crossing an object (e.g., where the line segments are around one or more objects rather than through the one or more objects). In some cases, the indirect path calculator  222  is configured to identify that no indirect path is available (e.g., where an audio source is fully occluded by objects). 
     The occlusion-based filter  226  may be configured to filter the audio signal  118  based on the indirect path  170 . For example, the occlusion-based filter  226  may be configured to filter the audio signal  118  based on sound diffraction characteristics associated with diffraction around one or more objects, such as the object  150 . The occlusion-based filter  226  may be configured to filter the audio signal  118  based on a frequency response (e.g., a cutoff frequency and a gain) associated with the object  150 , based on an angle of diffraction (e.g., an angle of the indirect path  170  relative to the direct path  160 ), or both. Filtering the audio signal  118  based on the indirect path  170  may simulate effects of frequency selectivity of the object  150  to sound propagating around the object  150 . As an example, if the object  150  has a particular sound diffraction characteristic, the occlusion-based filter  226  may filter the audio signal  118  using one or more corresponding filters (e.g., a low-pass filter, a high-pass filter, a band-pass filter, a band-reject filter, or a combination thereof). 
     The occlusion-based attenuator  230  may be configured to determine an attenuation associated with the indirect path  170  and to modify the audio signal  118  based on the attenuation. The occlusion-based attenuator  230  may be configured to determine the attenuation by determining a length of the indirect path  170 . In an illustrative example, the occlusion-based attenuator  230  may “roll off” amplitude of the audio signal  118  to simulate attenuation associated with sound travel through the indirect path  170 . 
     In a particular example, the occlusion-based attenuator  230  is configured to increase attenuation of the audio signal  118  if the indirect path  170  is through a boundary object. To illustrate, the occlusion-based attenuator  230  may be configured to compare the indirect path  170  (or coordinates associated with the indirect path  170 ) to the 3D map  112  to determine whether the indirect path  170  is through a boundary object, such as an open window or an open door. In a particular example, the occlusion-based attenuator  230  is configured to attenuate the audio signal  118  based on an opening size of the boundary object (e.g., where the opening size corresponds to a percentage of energy remaining in the audio signal  118  after attenuation of the audio signal  118 ). Alternatively or in addition, the image-based audio modifier  138  may be configured to perform one or more other operations based on the opening size, such as pitch shifting, damping, or equalization, as illustrative examples. 
     The binaural mixer  234  may be configured to mix the audio signal  118  based on the indirect path  170 . For example, the binaural mixer  234  may be configured to “mix” effects of the occlusion-based filter  226  and the occlusion-based attenuator  230 , such as by binaurally mixing the audio signal  118  between the left and right speakers/channels using a HRTF associated with the user in response to the direct path approaching each ear of the user at a given azimuthal angle and polar angle (e.g., as determined by the position and orientation of the user and as calculated using the indirect path  170 ). 
     The reverb synthesizer  206  is configured to reverberate the audio signal  118  based on an indication  202  of the first location  104  and further based on an indication  204  of the second location  106 . To illustrate, sound from the first location  104  may be associated with a higher order reflection characteristic (e.g., the set of reflections reaching the user after multiple reflections, which may correspond to the reflections that reach the user after a certain time threshold, such as approximately 5 to 100 milliseconds (ms) after generation of the sound, as an illustrative example). Sound from the first location  104  may also be associated with a lower order reflection characteristic (e.g., the set of reflections reaching the user after one or more reflections, which may correspond to the reflections that reach the user before a certain time threshold, such as approximately 5 to 100 ms after generation of the sound, as an illustrative example). The late reflection synthesizer  208  may be configured to modify the audio signal  118  to simulate reflections associated with the higher order reflection characteristic, and the early reflection synthesizer  210  may be configured to modify the audio signal  118  to simulate reflections associated with the lower order reflection characteristic. 
     The reverb synthesizer  206  may be configured to adjust the audio signal  118  based on characteristics that are relatively “static” (e.g., less sensitive to a user location, such as the second location  106 ) and also based on characteristics that are relatively “dynamic” (e.g., more sensitive to the user location). For example, the late reflection synthesizer  208  may be configured to adjust the audio signal  118  based on a higher order reflection characteristic associated with a room represented by the 3D map  112 . In this example, the higher order reflection characteristic associated with the room may be relatively “static” (e.g., where the higher order reverberation characteristic is uniform for different locations within the room). The early reflection synthesizer  210  may be configured to adjust the audio signal  118  based on a lower order reflection characteristic corresponding to the user location. The lower order reflection characteristic may be more “dynamic” and may be associated with sound reaching a listener “earlier” as compared to the higher order reflection characteristic (due to fewer reflections associated with the lower order reflection characteristic). The lower order reflection characteristic may also be directional and may be mixed binaurally. The lower order reflection characteristic may be more sensitive to user location and orientation as compared to the higher order reflection characteristic. 
     In some implementations, each room indicated by the 3D map  112  is associated with one or more “static” parameters, the audio signal  118  may be adjusted based on one more or static parameters associated with a particular room that includes the first location  104 , the second location  106 , or both. The reverb synthesizer  206  may be configured to identify, based on the first location  104  or the second location  106 , a particular room represented by the 3D map  112  and to determine one or more “static” parameters based on the particular room. 
     To further illustrate, the reverb synthesizer  206  may be configured to modify the audio signal  118  based on one or more parameters. The one or more parameters may include reverb density, reverb diffusion, late reverb gain, late reverb delay, late reverb panning, early reflection reverb gain, early reflection reverb delay, early reflection reverb panning, reverb high frequency gain, reverb low frequency gain, reverb decay time, a ratio of high frequency reverb decay to low frequency reverb decay, one or more other parameters, or a combination thereof. 
     In a particular example, reverb density, reverb diffusion, late reverb gain, reverb low frequency gain, reverb decay time, and the ratio of high frequency reverb decay to low frequency reverb decay may correspond to relatively “static” parameters. For example, each room indicated by the 3D map  112  may be associated with a respective reverb density, reverb diffusion, late reverb gain, reverb low frequency gain, reverb decay time, and ratio of high frequency reverb decay to low frequency reverb decay. 
     In some circumstances, a region including the first location  104  may differ from a room including the second location  106 . As a particular example, the first location  104  may be included in a first room, the second location  106  may be included in a second room. The object  150  may correspond to a door between the first room and the second room, and the audio signal  118  may be modified to include a knocking sound to simulate knocking at the door. The reverb synthesizer  206  may be configured to compare the indication  202  to the indication  204  to determine whether the locations  104 ,  106  correspond to a common room or to different rooms. In response to determining that the locations  104 ,  106  correspond to different rooms, the reverb synthesizer  206  may be configured to adjust late reverb gain, late reverb delay, late reverb panning (e.g., to simulate late reverberation characteristics associated with sound travel from one room to another room). 
     The reverb synthesizer  206  may be configured to adjust the audio signal  118  based on proximity of the AR device  102  (or a user of the AR device  102 ) to one or more surfaces. For example, the reverb synthesizer  206  may adjust one or more early reflection characteristics based on detecting that proximity of the AR device  102  to a surface is less than a threshold. To further illustrate, the AR device  102  may include an infrared sensor configured to determine proximity of the AR device  102  to a surface. In response to determining that the proximity is less than the threshold, the reverb synthesizer  206  may be configured to adjust early reflection reverb gain, early reflection reverb delay, early reflection reverb panning to simulate early reflection characteristics associated with reverberation from a surface proximate to a user of the AR device  102 . 
     In some implementations, the image-based audio modifier  138  is configured to store (or access) a sample library  190  including pre-recorded samples, such as “canned” sound effects and to add one or more samples of the sample library  190  to the audio signal  118  to modify the audio signal  118 . As an example, the sample library  190  may include different sounds such as knocking at a wooden table or door, footsteps on a wooden floor, the sound of objects being dragged across a concrete floor, or a creaking sound of a metal door opening, as illustrative examples. The AR device  102  may be configured to perform an image recognition operation using the second image data  110  to identify an object and to access the sample library  190  to retrieve one or more samples based on a type of the object. The image-based audio modifier  138  may be configured to modify the one or more samples based on one or more criteria detected in the second image data  110 . As an illustrative example, the image-based audio modifier  138  may perform one or more operations based on a detected (or estimated) size of a table or door, such as by adjusting one or more of amplitude, pitch, damping, or equalization, as illustrative examples. 
     In a particular example, one or more of the binaural mixers  212 ,  232 ,  234  are configured to adjust the audio signal  118  based on a head-related transfer function (HRTF) associated with a user of the AR device  102 . For example, the AR device  102  may be configured to binaurally mix the audio signal  118  so that the user perceives audio as originating from a particular location, such as the first location  104  (instead of as originating from the AR device  102 ). 
     In some implementations, the AR device  102  is configured to selectively activate one or more components of the image-based audio modifier  138 , such as based on the second image data  110 . To illustrate, in some cases, no object may be detected between the first location  104  and the second location  106 . In this case, the AR device  102  may temporarily power-down (e.g., deactivate or leave in a deactivated state) one or more of the indirect path calculator  222 , the occlusion-based filter  226 , the occlusion-based attenuator  230 , or the binaural mixer  234 . As another example, the AR device  102  may power-up one or more of the indirect path calculator  222 , the occlusion-based filter  226 , the occlusion-based attenuator  230 , or the binaural mixer  234  in response to detecting one or more objects (e.g., the object  150 ) using the second image data  110 . Thus, the AR device  102  may be configured to operate or more components of the image-based audio modifier  138  using a first power consumption (e.g., during an active mode of operation) and using a second power consumption (e.g., during a deactivated mode of operation) that is less than the first power consumption, such as by activating the one or more components “as needed” based on the second image data  110 . 
     After modifying the audio signal  118  to generate the modified audio signal  119 , the AR device  102  is configured to generate an acoustic representation of the modified audio signal  119 . For example, the audio sink  124  may include one or more speakers configured to generate an acoustic representation of the modified audio signal  119 . 
     Use of the image data  109 ,  110  and the 3D map  112  may increase efficiency of adjusting the audio signal  118  to simulate sound from the first location  104  to the second location  106 . As an example, use of the image data  109 ,  110  and the 3D map  112  may enable faster tracking of a dynamic environment (or a change between environments), such as where a user moves from inside to outside a building or from one room to another room of the building, which may be difficult or infeasible using other techniques. 
     Referring to  FIG. 3 , a particular illustrative example of a method is depicted and generally designated  300 . In a particular example, the method  300  is performed by an AR device, such as the AR device  102  of  FIGS. 1 and 2 . 
     The method  300  includes performing, at an AR device, a first image capture operation to generate first image data, at  302 . To illustrate, the image sensor  108  may perform the first image capture operation to generate multiple images (e.g., multiple separate images, a video stream, or both) included in the first image data  109 . In a particular example, the image sensor  108  performs the first image capture operation in connection with a walk-through calibration process (e.g., to “build” the 3D map  112 ). 
     The method  300  further includes determining, based on the first image data, a 3D map representing a set of locations including a first location, at  304 . For example, the 3D map  112  represents the first location  104 . In a particular example, determining the 3D map  112  includes stitching together multiple images included in the first image data  109  to generate a composite image of the set of locations using a panoramic image synthesis technique. In another example, the 3D map  112  may be received by the AR device  102  from another device, such as a server. For example, the AR device  102  may “offload” the first image data  109  to a server to generate the 3D map  112  and may receive the 3D map  112  from the server. 
     The method  300  further includes performing a second image capture operation to generate second image data at the AR device, at  306 . For example, the image sensor  108  may perform the second image capture operation to generate the second image data  110  in connection with a user mode of operation after the walk-through calibration process. The second image data  110  may include one image, multiple images, or a video stream, as illustrative examples. 
     The method  300  further includes identifying, based on the second image data, a second location of the set of locations, at  308 . For example, the AR device  102  may “match” one or more features indicated by the second image data  110  to one or more features indicated by the 3D map  112 . In a particular example, the AR device  102  may detect the “match” based on the features satisfying a similarity threshold. To further illustrate, the second location  106  may correspond to a user location of a user of the AR device  102 , and the first location may correspond to an audio source location of an audio source (e.g., the audio source  140 ) to be simulated. 
     The method  300  further includes modifying an audio signal at the AR device to synthesize (e.g., simulate) one or more acoustic characteristics associated with audio sent from the first location to the second location, at  310 . To illustrate, the audio signal  118  may be modified to generate the modified audio signal  119  by equalizing the audio signal  118 , binaurally mixing the audio signal  118 , artificially reverberating the audio signal  118 , performing one or more other operations, or a combination thereof. In a particular illustrative example, the method  300  further includes performing an image recognition operation to identify an object (e.g., a door) represented by the second image data  110 , and modifying the audio signal  118  includes accessing the sample library  190  (e.g., at the memory  134 ) based on the object and adding one or samples of the sample library to the audio signal  118  (e.g., by adding an impact-based sound, such as knocking sound, to the audio signal  118 ). 
     Use of the method  300  of  FIG. 3  may increase efficiency of adjusting an audio signal  118  to synthesize characteristics of sound from one location to another location. As an example, use of image data and a 3D map may enable faster tracking of a dynamic environment (or a change between environments), such as where a user moves from inside to outside a building or from one room to another room of the building, which may be difficult or infeasible using other techniques. 
     Referring to  FIG. 4 , a block diagram of a particular illustrative example of an electronic device is depicted and generally designated  400 . In an illustrative example, the electronic device  400  corresponds to an AR device, such as the AR device  102 . Alternatively or in addition, one or more aspects of the electronic device  400  may be implemented within a mobile device (e.g., a cellular phone), a computer (e.g., a server, a laptop computer, a tablet computer, or a desktop computer), an access point, a base station, a wearable electronic device (e.g., a personal camera, a head-mounted display, or a watch), a vehicle control system or console, an autonomous vehicle (e.g., a robotic car or a drone), a home appliance, a set top box, an entertainment device, a navigation device, a personal digital assistant (PDA), a television, a monitor, a tuner, a radio (e.g., a satellite radio), a music player (e.g., a digital music player or a portable music player), a video player (e.g., a digital video player, such as a digital video disc (DVD) player or a portable digital video player), a robot, a healthcare device, an Internet of Things (IoT) device, another electronic device, or a combination thereof. 
     The electronic device  400  includes one or more processors, such as a processor  410  (e.g., the processor  132  of  FIG. 1 ) and a graphics processing unit (GPU)  496 . The processor  410  may include a central processing unit (CPU), a digital signal processor (DSP), another processing device, or a combination thereof. 
     The electronic device  400  may further include one or more memories, such as a memory  432  (e.g., the memory  134  of  FIG. 1 ). The memory  432  may be coupled to the processor  410 , to the GPU  496 , or to both. The memory  432  may include random access memory (RAM), magnetoresistive random access memory (MRAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), one or more registers, a hard disk, a removable disk, a compact disc read-only memory (CD-ROM), another memory device, or a combination thereof. 
     The memory  432  may store instructions  460  (e.g., the instructions  136  of  FIG. 1 ). The instructions  460  may be executable by the processor  410 , by the GPU  496 , or by both. The instructions  460  may be executable to perform, initiate, or control one or more operations described herein, such as one or more operations described with reference to the method  300  of  FIG. 3 . 
     A CODEC  434  can also be coupled to the processor  410 . The CODEC  434  may be coupled to one or more microphones, such as a microphone  438 . The CODEC  434  may include a memory  418 . The memory  418  may store instructions  495  executable by the CODEC  434 . 
     In the example of  FIG. 4 , the processor  410  includes the image-based audio modifier  138 . In alternative implementations, one or more features of the image-based audio modifier  138  may be included in another component of the electronic device  400 , such as the CODEC  434 . 
     A speaker  436  may be coupled to the CODEC  434 . In a particular example, the speaker  436  corresponds to or is included in the audio sink  124  of  FIGS. 1 and 2 . The speaker  436  may be configured to generate an acoustic representation  470  of the modified audio signal  119 . In a particular illustrative example, the image-based audio modifier  138  is configured to generate the modified audio signal  119  based on the audio signal  118  and to provide the modified audio signal  119  to the CODEC  434 . The CODEC  434  may be configured to provide the modified audio signal  119  (or another version of the modified audio signal  119 , such as an analog version or a transcoded version of the modified audio signal  119 ) to the speaker  436  to generate the acoustic representation  470 . 
       FIG. 4  also shows a display controller  426  that is coupled to the processor  410  and to a display  428 . In a particular example, the display  428  has a “wrap around” configuration that is configured to sit on the bridge of the nose of a user. In this example, the electronic device  400  may be included in eyewear (e.g., googles) configured to be worn across the eyes of a user. 
     The display  428  may be configured to present graphics  490  associated with an AR application (e.g., an AR application corresponding to the instructions  460 ). The graphics  490  include a visual representation  492  associated with audio to be simulated by the modified audio signal  119 . As a particular illustrative example, the modified audio signal  119  may indicate a knocking sound of the acoustic representation  470 , and the visual representation  492  may include a depiction of a door corresponding to the audio source  140  of  FIG. 1 . The visual representation  492  may correspond to an actual object that is present at the first location  104  or a virtual object that is to be simulated at the first location  104 . 
     The electronic device  400  may further include a transmitter  482  coupled to an antenna  442 . The transmitter  482  may be configured to transmit an encoded signal  402 . Alternatively or in addition, the electronic device  400  may include a receiver  484  configured to receive the encoded signal  402 . The receiver  484  may be coupled to the antenna  442 , to one or more other antennas, or a combination thereof. In the example of  FIG. 4 , the transmitter  482  and the receiver  484  are included in a transceiver  440 . 
     In a particular example, the processor  410 , the GPU  496 , the memory  432 , the display controller  426 , the CODEC  434 , and the transceiver  440  are included in a system-on-chip (SoC) device  422 . Further, an input device  430  and a power supply  444  may be coupled to the SoC device  422 . Moreover, in a particular example, as illustrated in  FIG. 4 , the display  428 , the input device  430 , the speaker  436 , the microphone  438 , the antenna  442 , and the power supply  444  are external to the SoC device  422 . However, each of the display  428 , the input device  430 , the speaker  436 , the microphone  438 , the antenna  442 , and the power supply  444  can be coupled to a component of the SoC device  422 , such as to an interface or to a controller. 
     In conjunction with the described embodiments, a computer-readable medium (e.g., any of the memory  134 , the memory  418 , or the memory  432 ) stores instructions (e.g., any of the instructions  136 , the instructions  460 , or the instructions  495 ) executable by a processor (e.g., the processor  132 , the processor  410 , or the CODEC  434 ) to cause the processor to initiate, perform, or control operations. The operations include performing, at an AR device (e.g., the AR device  102  or the electronic device  400 ), a first image capture operation to generate first image data (e.g., the first image data  109 ). A 3D map (e.g., the 3D map  112 ) representing a set of locations including a first location (e.g., the first location  104 ) is determined based on the first image data. The operations further include performing a second image capture operation to generate second image data (e.g., the second image data  110 ) at the AR device. A second location (e.g., the second location  106 ) of the set of locations is identified based on the second image data. The operations further include modifying an audio signal (e.g., the audio signal  118 ) at the AR device to synthesize (e.g., simulate) one or more acoustic characteristics associated with audio sent from the first location to the second location. 
     In conjunction with the described embodiments, an apparatus includes means (e.g., any of the memories  134 ,  418 , and  432 ) for storing instructions (e.g., any of the instructions  136 , the instructions  460 , or the instructions  495 ) of an AR application. The apparatus further includes means (e.g., the processor  132  or the processor  410 ) for executing the instructions to initiate a first image capture operation to generate first image data (e.g., the first image data  109 ) and to determine a 3D map (e.g., the 3D map  112 ) based on the first image data. The 3D map represents a set of locations including a first location (e.g., the first location  104 ). The means for executing is configured to initiate a second image capture operation to generate second image data (e.g., the second image data  110 ), to execute the instructions to identify, based on the second image data, a second location (e.g., the second location  106 ) of the set of locations, and to modify an audio signal (e.g., the audio signal  118 ) to synthesize (e.g., simulate) one or more acoustic characteristics associated with audio sent from the first location to the second location. In a particular example, the apparatus further includes means (e.g., the image sensor  108 ) for performing the first image capture operation in connection with a walk-through calibration process and for performing the second image capture operation in connection with a user mode of operation. The apparatus may further include means (e.g., the audio sink  124 , the speaker  436 , or both) for generating an acoustic representation (e.g., the acoustic representation  470 ) of the modified audio signal. 
     As used herein, “coupled” may include communicatively coupled, electrically coupled, magnetically coupled, physically coupled, optically coupled, and combinations thereof. Two devices (or components) may be coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) directly or indirectly via one or more other devices, components, wires, buses, networks (e.g., a wired network, a wireless network, or a combination thereof), etc. Two devices (or components) that are electrically coupled may be included in the same device or in different devices and may be connected via electronics, one or more connectors, or inductive coupling, as illustrative, non-limiting examples. In some implementations, two devices (or components) that are communicatively coupled, such as in electrical communication, may send and receive electrical signals (digital signals or analog signals) directly or indirectly, such as via one or more wires, buses, networks, etc. 
     As used herein, “generating,” “calculating,” “using,” “selecting,” “accessing,” and “determining” may be used interchangeably. For example, “generating,” “calculating,” or “determining” a value, a characteristic, a parameter, or a signal may refer to actively generating, calculating, or determining a value, a characteristic, a parameter, or a signal or may refer to using, selecting, or accessing a value, a characteristic, a parameter, or a signal that is already generated, such as by a component or a device. 
     The foregoing disclosed devices and functionalities may be designed and represented using computer files (e.g. RTL, GDSII, GERBER, etc.). The computer files may be stored on computer-readable media. Some or all such files may be provided to fabrication handlers who fabricate devices based on such files. Resulting products include wafers that are then cut into die and packaged into integrated circuits (or “chips”). The integrated circuits are then employed in electronic devices, such as the AR device  102  of  FIG. 1  and the electronic device  400  of  FIG. 4 . 
     The various illustrative logical blocks, configurations, modules, circuits, and algorithm steps described in connection with the examples disclosed herein may be implemented as electronic hardware, computer software executed by a processor, or combinations of both. Various illustrative components, blocks, configurations, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or processor executable instructions depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. 
     One or more operations of a method or algorithm described herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. For example, one or more operations of the method  300  of  FIG. 3  may be initiated, controlled, or performed by a field-programmable gate array (FPGA) device, an application-specific integrated circuit (ASIC), a processing unit such as a central processing unit (CPU), a digital signal processor (DSP), a controller, another hardware device, a firmware device, or a combination thereof. A software module may reside in random access memory (RAM), magnetoresistive random access memory (MRAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disk, a removable disk, a compact disc read-only memory (CD-ROM), or any other form of non-transitory storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). The ASIC may reside in a computing device or a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a computing device or user terminal. 
     The previous description of the disclosed examples is provided to enable a person skilled in the art to make or use the disclosed examples. Various modifications to these examples will readily apparent to those skilled in the art, and the principles defined herein may be applied to other examples without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the examples shown herein but is to be accorded the widest scope possible consistent with the principles and novel features as defined by the following claims.