Patent Publication Number: US-2022232342-A1

Title: Audio system for artificial reality applications

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims a priority and benefit to U.S. Provisional Patent Application Ser. No. 63/191,865, filed May 21, 2021, U.S. Provisional Patent Application Ser. No. 63/209,315, filed Jun. 10, 2021, U.S. Provisional Patent Application Ser. No. 63/248,924, filed Sep. 27, 2021, and U.S. Provisional Patent Application Ser. No. 63/252,014, filed Oct. 4, 2021, each of which is hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The present disclosure relates generally to artificial reality systems, and specifically relates to an audio system for artificial reality applications. 
     BACKGROUND 
     In artificial reality applications, it is commonly desired to create a virtual sound to convince a user of an eyewear device (e.g., smart glasses) that a sound source is near a user&#39;s ear. This is typically hard to achieve for an eyewear device with open ear fitting, as the sound source is usually located in a temple arm of the eyewear device. Additionally, an audio output in a form factor of an eyewear device (e.g., audio glasses) has very tight constraints in relation to a size, power and weight. For the audio glasses design, there is a huge push towards trimming a size of the temple arm to make it more stylish (socially acceptable) and light weight. 
     In many digital signal processing and control applications, an amount of time TDSP used by a processor to produce an output sample y[k] from an input sample x[k] can degrade performance characteristics of a system being controlled by the processor. Therefore, keeping the amount of time TDSP below design limits is desirable. In addition, a frequency at which the input sample x[k] is processed—for producing an associated output sample y[k]—also contributes to the degradation of performance characteristics. Such a frequency is commonly referred to as a sampling frequency Fs with corresponding sampling period Ts=1/Fs, and thus keeping Ts below design limits is also desirable. Consequently, TDSP becomes constrained to an upper limit value not to exceed Ts, often requiring the processor to operate at extremely high clock rates and/or to execute multiple instructions in parallel, which could result in elevated power consumption dissipated as heat. 
     A task of multichannel speech enhancement is to improve the intelligibility and quality of a noisy speech by utilizing recording from multiple microphones. Traditional approaches use linear spatial filters, such as a minimum variance distortionless response (MVDR) beamformer, designed to preserve signals coming from a target source direction and attenuate signals from other directions. The approaches based on MVDR beamformer utilize spatial correlations of speech and noise to determine filter coefficients, which is convenient to use with unknown array geometries. 
     Another approach for multichannel speech enhancement is a supervised speech enhancement that utilizes deep neural networks (DNNs). For multichannel processing, DNNs are well suited for fixed array geometries. However, DNNs are not well suited for ad-hoc array processing as the DNNs require the input and the output size to be fixed. Ad-hoc array processing requires a network to be able to process a multichannel signal with unknown number of microphones and in any order. In other words, the network should be invariant to the number and the order of microphones. One approach to design such networks is to use processing blocks that are number and permutation invariant, such as global pooling and self-attention. Another approach is to use a novel transform average and concatenate module to handle unknown number and order of microphones. Yet another approach is to utilize a spatial-temporal network where a recurrent network is used for temporal processing and self-attention is used for spatial processing. An output corresponding to a target microphone is obtained by using a global pooling layer. Yet another approach is utilizing a two stage deep ad-hoc beamforming. In the first stage, top k microphones are selected. In the second stage, selected k signals are used for k-microphone speech enhancement. 
     For multichannel speech enhancement processing, DNNs are generally incorporated with traditional spatial filters. Another approach is to train a DNN with spatial features, such as inter-channel phase, time, and level difference. Yet another approach is based on end-to-end supervised training without any explicit spatial filtering. An end-to-end supervised model when combined with a traditional spatial filter (e.g., MVDR beamformer) and a DNN-based post filter can provide additional consistent and significant improvements when compared with a DNN-only one-stage or multistage scheme. 
     SUMMARY 
     Embodiments of the present disclosure relate to an audio system for artificial reality applications. The audio system includes one or more transducers coupled to a headset and a controller coupled to the one or more transducers. The one or more transducers are configured to output, in accordance with audio instructions, one or more ultrasonic pressure waves simulating a virtual audio source near an ear of a user of the headset. The controller is configured to generate the audio instructions such that the one or more ultrasonic pressure waves form at least a portion of audio content for presentation to the user. 
     Embodiments of the present disclosure further relate to a method for digital signal processing. A set of signal samples is divided into a first set of samples and a second set of samples, wherein the first set of samples are associated with a more recent set of time values than the second set of samples. The first set of samples is processed using a first digital signal processor (DSP) of the headset. The second set of samples is processed using a second DSP of the headset, wherein the second DSP is slower than the first DSP. Outputs from the first DSP and the second DSP are combined to form a combined output for presentation to the user, the combined output representative of a filtering operation being applied to the set of signal samples. 
     Embodiments of the present disclosure further relate to an audio system of a headset for enhancing audio presented to a user of the headset. The audio system includes an array of microphones, a deep neural network (DNN) coupled to the array of microphones, and one or more transducers coupled to the DNN. The array of microphones are configured to detect audio signals in a local area of the headset. The DNN is configured to process the detected audio signals to generate enhanced audio content. The one or more transducers are configured to present the enhanced audio content to the user. 
     Embodiments of the present disclosure further relate to a method performed by an audio system of a headset for enhancing audio presented to a user of the headset. Audio signals are first detected via an array of microphones of the audio system. The detected audio signals are then processed using a DNN of the audio system to generate enhanced audio content. The enhanced audio content is presented to the user via one or more transducers of the audio system. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  is a perspective view of a headset implemented as an eyewear device, in accordance with one or more embodiments. 
         FIG. 1B  is a perspective view of a headset implemented as a head-mounted display, in accordance with one or more embodiments. 
         FIG. 2  is a block diagram of an audio system, in accordance with one or more embodiments. 
         FIG. 3A  illustrates an example presentation of near ear virtual sounds to a user of an audio system, in accordance with one or more embodiments. 
         FIG. 3B  illustrates an example usage of phased array speakers for generating haptics and audio output, in accordance with one or more embodiments. 
         FIG. 4  is an example top view of a capacitive micromachined ultrasonic transducer, in accordance with one or more embodiments. 
         FIG. 5A  illustrates an example topology of a piezoelectric micromachined ultrasonic transducer (PMUT), in accordance with one or more embodiments. 
         FIG. 5B  illustrates an example array of PMUTs, in accordance with one or more embodiments. 
         FIG. 5C  illustrates an example cross-section of a PMUT, in accordance with one or more embodiments. 
         FIG. 5D  illustrates an example top view of a first PMUT array, in accordance with one or more embodiments. 
         FIG. 5E  illustrates an example top view of a second PMUT array, in accordance with one or more embodiments. 
         FIG. 6  is a block diagram of a triple-path attentive recurrent network (TPARN) for an ad-hoc array multichannel speech enhancement, in accordance with one or more embodiments. 
         FIG. 7  is a block diagram of the TPARN architecture, in accordance with one or more embodiments. 
         FIG. 8A  is a block diagram of a recurrent neural network in the TPARN architecture, in accordance with one or more embodiments. 
         FIG. 8B  is a block diagram of an attention block in the TPARN architecture, in accordance with one or more embodiments. 
         FIG. 8C  is a block diagram of a feedforward block in the TPARN architecture, in accordance with one or more embodiments. 
         FIG. 9  is a block diagram of an attentive dense convolutional network (ADCN) for multichannel speech enhancement, in accordance with one or more embodiments. 
         FIG. 10A  is a block diagram of a dense block of the ADCN, in accordance with one or more embodiments. 
         FIG. 10B  is a block diagram an attention block of the ADCN, in accordance with one or more embodiments. 
         FIG. 11A  is a block diagram of a two-stage sound-enhancement architecture with a minimum variance distortionless response (MVDR) beamformer, in accordance with one or more embodiments. 
         FIG. 11B  is a block diagram of a two-stage sound-enhancement architecture without a MVDR beamformer, in accordance with one or more embodiments. 
         FIG. 12A  illustrates an example architecture for splitting a convolution calculation between two digital signal processing (DSP) paths, in accordance with one or more embodiments. 
         FIG. 12B  illustrates an example timing diagram for the convolution split of  FIG. 12A , in accordance with one or more embodiments. 
         FIG. 13  illustrates an example block diagram of a coefficient bank switching control, in accordance with one or more embodiments. 
         FIG. 14A  illustrates an example signal processing architecture with a quantizer for splitting a convolution calculation between two DSP paths, in accordance with one or more embodiments. 
         FIG. 14B  illustrates an example signal processing architecture with a nonlinear quantization for splitting a convolution calculation between two DSP paths, in accordance with one or more embodiments. 
         FIG. 15  is a flowchart illustrating a process for multi-channel speech enhancement at an audio system, in accordance with one or more embodiments, in accordance with one or more embodiments. 
         FIG. 16  depicts a block diagram of a system that includes a headset, in accordance with one or more embodiments. 
     
    
    
     The figures depict various embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein. 
     DETAILED DESCRIPTION 
     Some embodiments of the present disclosure are related to an audio system that uses a triple-path attentive recurrent network (TPARN) for ad-hoc multichannel speech enhancement in time-domain. The TPARN is designed by extending a single-channel dual-path model to a multichannel model by adding a third-path along a spatial dimension. A simple but effective attention along the channels is proposed to make TPARN suitable for ad-hoc array processing, i.e., the number of microphones and order invariant processing. The audio system may utilize a deep neural network (DNN) to directly (i.e., without a beamformer) enhance sound. In some embodiments, the DNN is a TPARN that has a multiple-input multiple-output (MIMO) architecture. The audio system receives signals from a microphone array, processes the received signals using the DNN to enhance one or more sounds (e.g., voice) in the local area. In some embodiments, the DNN may be trained using microphones in random locations and/or random numbers of microphones. In some embodiments, the DNN is part of a multi-stage system. For example, a first stage may be an attentive dense convolutional network (ADCN) model followed by the TPARN in the second stage. 
     In some embodiments, the audio system as part of a headset uses an array of micromachined ultrasound transducers (capacitive and/or piezoelectric) to provide content to a user in an audible range. The audio system uses ultrasound to generate a virtual sounds source that also provides haptic feedback to a user. The audio system includes a phased array of ultrasonic speakers that can output ultrasonic pressure waves that combine in a manner that simulates a virtual audio source near an ear of the user. This virtual audio source can be made to be quite close to the ear such that it appears that the user experiences haptics from the sound. In this manner, the presented audio may sound and feel like it is being whispered into an ear of the user. 
     In some embodiments, the audio system has an architecture that is based on distributing computation of an algorithm among slow and fast digital signal processing paths. A portion of the algorithm dependent on more recent information is executed on a fast processing path. A portion of the algorithm dependent on older information is executed on a slow processing path in such a way that outputs of each path become ready to be combined and generate the desired algorithm output in a timely manner. 
     Embodiments of the present disclosure may include or be implemented in conjunction with an artificial reality system. Artificial reality is a form of reality that has been adjusted in some manner before presentation to a user, which may include, e.g., a virtual reality (VR), an augmented reality (AR), a mixed reality (MR), a hybrid reality, or some combination and/or derivatives thereof. Artificial reality content may include completely generated content or generated content combined with captured (e.g., real-world) content. The artificial reality content may include video, audio, haptic feedback, or some combination thereof, any of which may be presented in a single channel or in multiple channels (such as stereo video that produces a three-dimensional effect to the viewer). Additionally, in some embodiments, artificial reality may also be associated with applications, products, accessories, services, or some combination thereof, that are used to create content in an artificial reality and/or are otherwise used in an artificial reality. The artificial reality system that provides the artificial reality content may be implemented on various platforms, including a wearable device (e.g., headset) connected to a host computer system, a standalone wearable device (e.g., headset), a mobile device or computing system, or any other hardware platform capable of providing artificial reality content to one or more viewers. 
       FIG. 1A  is a perspective view of a headset  100  implemented as an eyewear device, in accordance with one or more embodiments. In some embodiments, the eyewear device is a near eye display (NED). In general, the headset  100  may be worn on the face of a user such that content (e.g., media content) is presented using a display assembly and/or an audio system. However, the headset  100  may also be used such that media content is presented to a user in a different manner. Examples of media content presented by the headset  100  include one or more images, video, audio, or some combination thereof. The headset  100  includes a frame  110 , and may include, among other components, a display assembly including one or more display elements  120 , a depth camera assembly (DCA), an audio system, and a position sensor  190 . While  FIG. 1A  illustrates the components of the headset  100  in example locations on the headset  100 , the components may be located elsewhere on the headset  100 , on a peripheral device paired with the headset  100 , or some combination thereof. Similarly, there may be more or fewer components on the headset  100  than what is shown in  FIG. 1A . 
     The frame  110  holds the other components of the headset  100 . The frame  110  includes a front part that holds the one or more display elements  120  and end pieces (e.g., temples) to attach to a head of the user. The front part of the frame  110  bridges the top of a nose of the user. The length of the end pieces may be adjustable (e.g., adjustable temple length) to fit different users. The end pieces may also include a portion that curls behind the ear of the user (e.g., temple tip, earpiece). 
     The one or more display elements  120  provide light to a user wearing the headset  100 . As illustrated in  FIG. 1A , the headset includes a display element  120  for each eye of a user. In some embodiments, a display element  120  generates image light that is provided to an eye box of the headset  100 . The eye box is a location in space that an eye of the user occupies while wearing the headset  100 . For example, a display element  120  may be a waveguide display. A waveguide display includes a light source (e.g., a two-dimensional source, one or more line sources, one or more point sources, etc.) and one or more waveguides. Light from the light source is in-coupled into the one or more waveguides which outputs the light in a manner such that there is pupil replication in an eye box of the headset  100 . In-coupling and/or outcoupling of light from the one or more waveguides may be done using one or more diffraction gratings. In some embodiments, the waveguide display includes a scanning element (e.g., waveguide, mirror, etc.) that scans light from the light source as it is in-coupled into the one or more waveguides. Note that in some embodiments, one or both of the display elements  120  are opaque and do not transmit light from a local area around the headset  100 . The local area is the area surrounding the headset  100 . For example, the local area may be a room that a user wearing the headset  100  is inside, or the user wearing the headset  100  may be outside and the local area is an outside area. In this context, the headset  100  generates VR content. Alternatively, in some embodiments, one or both of the display elements  120  are at least partially transparent, such that light from the local area may be combined with light from the one or more display elements to produce AR and/or MR content. 
     In some embodiments, a display element  120  does not generate image light, and instead is a lens that transmits light from the local area to the eye box. For example, one or both of the display elements  120  may be a lens without correction (non-prescription) or a prescription lens (e.g., single vision, bifocal and trifocal, or progressive) to help correct for defects in a user&#39;s eyesight. In some embodiments, the display element  120  may be polarized and/or tinted to protect the user&#39;s eyes from the sun. 
     In some embodiments, the display element  120  may include an additional optics block (not shown). The optics block may include one or more optical elements (e.g., lens, Fresnel lens, etc.) that direct light from the display element  120  to the eye box. The optics block may, e.g., correct for aberrations in some or all of the image content, magnify some or all of the image, or some combination thereof. 
     The DCA determines depth information for a portion of a local area surrounding the headset  100 . The DCA includes one or more imaging devices  130  and a DCA controller (not shown in  FIG. 1A ), and may also include an illuminator  140 . In some embodiments, the illuminator  140  illuminates a portion of the local area with light. The light may be, e.g., structured light (e.g., dot pattern, bars, etc.) in the infrared (IR), IR flash for time-of-flight, etc. In some embodiments, the one or more imaging devices  130  capture images of the portion of the local area that include the light from the illuminator  140 . As illustrated,  FIG. 1A  shows a single illuminator  140  and two imaging devices  130 . In alternate embodiments, there is no illuminator  140  and at least two imaging devices  130   
     The DCA controller computes depth information for the portion of the local area using the captured images and one or more depth determination techniques. The depth determination technique may be, e.g., direct time-of-flight (ToF) depth sensing, indirect ToF depth sensing, structured light, passive stereo analysis, active stereo analysis (uses texture added to the scene by light from the illuminator  140 ), some other technique to determine depth of a scene, or some combination thereof. 
     The audio system provides audio content. The audio system includes a transducer array, a sensor array, and an audio controller  150 . However, in other embodiments, the audio system may include different and/or additional components. Similarly, in some cases, functionality described with reference to the components of the audio system can be distributed among the components in a different manner than is described here. For example, some or all of the functions of the audio controller  150  may be performed by a remote server. 
     The transducer array presents sound to user. The transducer array includes a plurality of transducers. A transducer may be a speaker  160  or a tissue transducer  170  (e.g., a bone conduction transducer or a cartilage conduction transducer). Although the speakers  160  are shown exterior to the frame  110 , the speakers  160  may be enclosed in the frame  110 . The tissue transducer  170  couples to the head of the user and directly vibrates tissue (e.g., bone or cartilage) of the user to generate sound. In accordance with embodiments of the present disclosure, the transducer array comprises two transducers (e.g., two speakers  160 , two tissue transducers  170 , or one speaker  160  and one tissue transducer  170 ), i.e., one transducer for each ear. The locations of transducers may be different from what is shown in  FIG. 1A . 
     The sensor array detects sounds within the local area of the headset  100 . The sensor array includes a plurality of acoustic sensors  180 . An acoustic sensor  180  captures sounds emitted from one or more sound sources in the local area (e.g., a room). Each acoustic sensor is configured to detect sound and convert the detected sound into an electronic format (analog or digital). The acoustic sensors  180  may be acoustic wave sensors, microphones, sound transducers, or similar sensors that are suitable for detecting sounds. 
     In some embodiments, one or more acoustic sensors  180  may be placed in an ear canal of each ear (e.g., acting as binaural microphones). In some embodiments, the acoustic sensors  180  may be placed on an exterior surface of the headset  100 , placed on an interior surface of the headset  100 , separate from the headset  100  (e.g., part of some other device), or some combination thereof. The number and/or locations of acoustic sensors  180  may be different from what is shown in  FIG. 1A . For example, the number of acoustic detection locations may be increased to increase the amount of audio information collected and the sensitivity and/or accuracy of the information. The acoustic detection locations may be oriented such that the microphone is able to detect sounds in a wide range of directions surrounding the user wearing the headset  100 . 
     The audio controller  150  processes information from the sensor array that describes sounds detected by the sensor array. The audio controller  150  may comprise a processor and a non-transitory computer-readable storage medium. The audio controller  150  may be configured to generate direction of arrival (DOA) estimates, generate acoustic transfer functions (e.g., array transfer functions and/or head-related transfer functions), track the location of sound sources, form beams in the direction of sound sources, classify sound sources, generate sound filters for the speakers  160 , or some combination thereof. 
     In some embodiments, the audio system is fully integrated into the headset  100 . In some other embodiments, the audio system is distributed among multiple devices, such as between a computing device (e.g., smart phone or a console) and the headset  100 . The computing device may be interfaced (e.g., via a wired or wireless connection) with the headset  100 . In such cases, some of the processing steps presented herein may be performed at a portion of the audio system integrated into the computing device. For example, one or more functions of the audio controller  150  may be implemented at the computing device. More details about the structure and operations of the audio system are described in connection with  FIG. 2 . 
     The position sensor  190  generates one or more measurement signals in response to motion of the headset  100 . The position sensor  190  may be located on a portion of the frame  110  of the headset  100 . The position sensor  190  may include an IMU. Examples of position sensor  190  include: one or more accelerometers, one or more gyroscopes, one or more magnetometers, another suitable type of sensor that detects motion, a type of sensor used for error correction of the IMU, or some combination thereof. The position sensor  190  may be located external to the IMU, internal to the IMU, or some combination thereof. 
     The audio system can use positional information describing the headset  100  (e.g., from the position sensor  190 ) to update virtual positions of sound sources so that the sound sources are positionally locked relative to the headset  100 . In this case, when the user wearing the headset  100  turns their head, virtual positions of the virtual sources move with the head. Alternatively, virtual positions of the virtual sources are not locked relative to an orientation of the headset  100 . In this case, when the user wearing the headset  100  turns their head, apparent virtual positions of the sound sources would not change. 
     In some embodiments, the headset  100  may provide for simultaneous localization and mapping (SLAM) for a position of the headset  100  and updating of a model of the local area. For example, the headset  100  may include a passive camera assembly (PCA) that generates color image data. The PCA may include one or more RGB cameras that capture images of some or all of the local area. In some embodiments, some or all of the imaging devices  130  of the DCA may also function as the PCA. The images captured by the PCA and the depth information determined by the DCA may be used to determine parameters of the local area, generate a model of the local area, update a model of the local area, or some combination thereof. Furthermore, the position sensor  190  tracks the position (e.g., location and pose) of the headset  100  within the room. Additional details regarding the components of the headset  100  are discussed below in connection with  FIG. 2  and  FIG. 16 . 
       FIG. 1B  is a perspective view of a headset  105  implemented as a head-mounted display (HMD), in accordance with one or more embodiments. In embodiments that describe an AR system and/or a MR system, portions of a front side of the HMD are at least partially transparent in the visible band (˜380 nm to 750 nm), and portions of the HMD that are between the front side of the HMD and an eye of the user are at least partially transparent (e.g., a partially transparent electronic display). The HMD includes a front rigid body  115  and a band  175 . The headset  105  includes many of the same components described above with reference to  FIG. 1A  but modified to integrate with the HMD form factor. For example, the HMD includes a display assembly, a DCA, an audio system, and a position sensor  190 .  FIG. 1B  shows the illuminator  140 , a plurality of the speakers  160 , a plurality of the imaging devices  130 , a plurality of acoustic sensors  180 , and the position sensor  190 . The speakers  160  may be located in various locations, such as coupled to the band  175  (as shown), coupled to the front rigid body  115 , or may be configured to be inserted within the ear canal of a user. 
       FIG. 2  is a block diagram of an audio system  200 , in accordance with one or more embodiments. The audio system in  FIG. 1A  or  FIG. 1B  may be an embodiment of the audio system  200 . The audio system  200  generates one or more acoustic transfer functions for a user. The audio system  200  may then use the one or more acoustic transfer functions to generate audio content for the user. In the embodiment of  FIG. 2 , the audio system  200  includes a transducer array  210 , a sensor array  220 , and an audio controller  230 . Some embodiments of the audio system  200  have different components than those described here. Similarly, in some cases, functions can be distributed among the components in a different manner than is described here. 
     The transducer array  210  is configured to present audio content. The transducer array  210  includes a pair of transducers, i.e., one transducer for each ear. A transducer is a device that provides audio content. A transducer may be, e.g., a speaker (e.g., the speaker  160 ), a tissue transducer (e.g., the tissue transducer  170 ), some other device that provides audio content, or some combination thereof. A tissue transducer may be configured to function as a bone conduction transducer or a cartilage conduction transducer. The transducer array  210  may present audio content via air conduction (e.g., via one or two speakers), via bone conduction (via one or two bone conduction transducer), via cartilage conduction audio system (via one or two cartilage conduction transducers), or some combination thereof. 
     The bone conduction transducers generate acoustic pressure waves by vibrating bone/tissue in the user&#39;s head. A bone conduction transducer may be coupled to a portion of a headset, and may be configured to be behind the auricle coupled to a portion of the user&#39;s skull. The bone conduction transducer receives vibration instructions from the audio controller  230 , and vibrates a portion of the user&#39;s skull based on the received instructions. The vibrations from the bone conduction transducer generate a tissue-borne acoustic pressure wave that propagates toward the user&#39;s cochlea, bypassing the eardrum. 
     The cartilage conduction transducers generate acoustic pressure waves by vibrating one or more portions of the auricular cartilage of the ears of the user. A cartilage conduction transducer may be coupled to a portion of a headset, and may be configured to be coupled to one or more portions of the auricular cartilage of the ear. For example, the cartilage conduction transducer may couple to the back of an auricle of the ear of the user. The cartilage conduction transducer may be located anywhere along the auricular cartilage around the outer ear (e.g., the pinna, the tragus, some other portion of the auricular cartilage, or some combination thereof). Vibrating the one or more portions of auricular cartilage may generate: airborne acoustic pressure waves outside the ear canal; tissue born acoustic pressure waves that cause some portions of the ear canal to vibrate thereby generating an airborne acoustic pressure wave within the ear canal; or some combination thereof. The generated airborne acoustic pressure waves propagate down the ear canal toward the ear drum. 
     The transducer array  210  generates audio content in accordance with instructions from the audio controller  230 . In some embodiments, the audio content is spatialized. Spatialized audio content is audio content that appears to originate from a particular direction and/or target region (e.g., an object in the local area and/or a virtual object). For example, spatialized audio content can make it appear that sound is originating from a virtual singer across a room from a user of the audio system  200 . The transducer array  210  may be coupled to a wearable device (e.g., the headset  100  or the headset  105 ). In alternate embodiments, the transducer array  210  may be a pair of speakers that are separate from the wearable device (e.g., coupled to an external console). 
     The sensor array  220  detects sounds within a local area surrounding the sensor array  220 . The sensor array  220  may include a plurality of acoustic sensors that each detect air pressure variations of a sound wave and convert the detected sounds into an electronic format (analog or digital). The plurality of acoustic sensors may be positioned on a headset (e.g., headset  100  and/or the headset  105 ), on a user (e.g., in an ear canal of the user), on a neckband, or some combination thereof. An acoustic sensor may be, e.g., a microphone, a vibration sensor, an accelerometer, or any combination thereof. In some embodiments, the sensor array  220  is configured to monitor the audio content generated by the transducer array  210  using at least some of the plurality of acoustic sensors. Increasing the number of sensors may improve the accuracy of information (e.g., directionality) describing a sound field produced by the transducer array  210  and/or sound from the local area. 
     The audio controller  230  controls operation of the audio system  200 . In the embodiment of  FIG. 2 , the audio controller  230  includes a data store  235 , a DOA estimation module  240 , a transfer function module  250 , a tracking module  260 , a beamforming module  270 , and a sound filter module  280 . The audio controller  230  may be located inside a headset, in some embodiments. Some embodiments of the audio controller  230  have different components than those described here. Similarly, functions can be distributed among the components in different manners than described here. For example, some functions of the audio controller  230  may be performed external to the headset. The user may opt in to allow the audio controller  230  to transmit data captured by the headset to systems external to the headset, and the user may select privacy settings controlling access to any such data. 
     The data store  235  stores data for use by the audio system  200 . Data in the data store  235  may include sounds recorded in the local area of the audio system  200 , audio content, HRTFs, transfer functions for one or more sensors, array transfer functions (ATFs) for one or more of the acoustic sensors, sound source locations, virtual model of local area, direction of arrival estimates, sound filters, virtual positions of sound sources, multi-source audio signals, signals for transducers (e.g., speakers) for each ear, and other data relevant for use by the audio system  200 , or any combination thereof. The data store  235  may be implemented as a non-transitory computer-readable storage medium. 
     The user may opt-in to allow the data store  235  to record data captured by the audio system  200 . In some embodiments, the audio system  200  may employ always on recording, in which the audio system  200  records all sounds captured by the audio system  200  in order to improve the experience for the user. The user may opt in or opt out to allow or prevent the audio system  200  from recording, storing, or transmitting the recorded data to other entities. 
     The DOA estimation module  240  is configured to localize sound sources in the local area based in part on information from the sensor array  220 . Localization is a process of determining where sound sources are located relative to the user of the audio system  200 . The DOA estimation module  240  performs a DOA analysis to localize one or more sound sources within the local area. The DOA analysis may include analyzing the intensity, spectra, and/or arrival time of each sound at the sensor array  220  to determine the direction from which the sounds originated. In some cases, the DOA analysis may include any suitable algorithm for analyzing a surrounding acoustic environment in which the audio system  200  is located. 
     For example, the DOA analysis may be designed to receive input signals from the sensor array  220  and apply digital signal processing algorithms to the input signals to estimate a direction of arrival. These algorithms may include, for example, delay and sum algorithms where the input signal is sampled, and the resulting weighted and delayed versions of the sampled signal are averaged together to determine a DOA. A least mean squared (LMS) algorithm may also be implemented to create an adaptive filter. This adaptive filter may then be used to identify differences in signal intensity, for example, or differences in time of arrival. These differences may then be used to estimate the DOA. In another embodiment, the DOA may be determined by converting the input signals into the frequency domain and selecting specific bins within the time-frequency (TF) domain to process. Each selected TF bin may be processed to determine whether that bin includes a portion of the audio spectrum with a direct path audio signal. Those bins having a portion of the direct-path signal may then be analyzed to identify the angle at which the sensor array  220  received the direct-path audio signal. The determined angle may then be used to identify the DOA for the received input signal. Other algorithms not listed above may also be used alone or in combination with the above algorithms to determine DOA. 
     In some embodiments, the DOA estimation module  240  may also determine the DOA with respect to an absolute position of the audio system  200  within the local area. The position of the sensor array  220  may be received from an external system (e.g., some other component of a headset, an artificial reality console, a mapping server, a position sensor (e.g., the position sensor  190 , etc.). The external system may create a virtual model of the local area, in which the local area and the position of the audio system  200  are mapped. The received position information may include a location and/or an orientation of some or all of the audio system  200  (e.g., of the sensor array  220 ). The DOA estimation module  240  may update the estimated DOA based on the received position information. 
     The transfer function module  250  is configured to generate one or more acoustic transfer functions. Generally, a transfer function is a mathematical function giving a corresponding output value for each possible input value. Based on parameters of the detected sounds, the transfer function module  250  generates one or more acoustic transfer functions associated with the audio system. The acoustic transfer functions may be ATFs, HRTFs, other types of acoustic transfer functions, or some combination thereof. An ATF characterizes how the microphone receives a sound from a point in space. 
     An ATF includes a number of transfer functions that characterize a relationship between the sound source and the corresponding sound received by the acoustic sensors in the sensor array  220 . Accordingly, for a sound source there is a corresponding transfer function for each of the acoustic sensors in the sensor array  220 . And collectively the set of transfer functions is referred to as an ATF. Accordingly, for each sound source there is a corresponding ATF. Note that the sound source may be, e.g., someone or something generating sound in the local area, the user, or one or more transducers of the transducer array  210 . The ATF for a particular sound source location relative to the sensor array  220  may differ from user to user due to a person&#39;s anatomy (e.g., ear shape, shoulders, etc.) that affects the sound as it travels to the person&#39;s ears. Accordingly, the ATFs of the sensor array  220  are personalized for each user of the audio system  200 . 
     In some embodiments, the transfer function module  250  determines one or more HRTFs for a user of the audio system  200 . The HRTF characterizes how an ear receives a sound from a point in space. The HRTF for a particular source location relative to a person is unique to each ear of the person (and is unique to the person) due to the person&#39;s anatomy (e.g., ear shape, shoulders, etc.) that affects the sound as it travels to the person&#39;s ears. In some embodiments, the transfer function module  250  may determine HRTFs for the user using a calibration process. In some embodiments, the transfer function module  250  may provide information about the user to a remote system. The user may adjust privacy settings to allow or prevent the transfer function module  250  from providing the information about the user to any remote systems. The remote system determines a set of HRTFs that are customized to the user using, e.g., machine learning, and provides the customized set of HRTFs to the audio system  200 . 
     The tracking module  260  is configured to track locations of one or more sound sources. The tracking module  260  may compare current DOA estimates and compare them with a stored history of previous DOA estimates. In some embodiments, the audio system  200  may recalculate DOA estimates on a periodic schedule, such as once per second, or once per millisecond. The tracking module may compare the current DOA estimates with previous DOA estimates, and in response to a change in a DOA estimate for a sound source, the tracking module  260  may determine that the sound source moved. In some embodiments, the tracking module  260  may detect a change in location based on visual information received from the headset or some other external source. The tracking module  260  may track the movement of one or more sound sources over time. The tracking module  260  may store values for a number of sound sources and a location of each sound source at each point in time. In response to a change in a value of the number or locations of the sound sources, the tracking module  260  may determine that a sound source moved. The tracking module  260  may calculate an estimate of the localization variance. The localization variance may be used as a confidence level for each determination of a change in movement. 
     The beamforming module  270  is configured to process one or more ATFs to selectively emphasize sounds from sound sources within a certain area while de-emphasizing sounds from other areas. In analyzing sounds detected by the sensor array  220 , the beamforming module  270  may combine information from different acoustic sensors to emphasize sound associated from a particular region of the local area while deemphasizing sound that is from outside of the region. The beamforming module  270  may isolate an audio signal associated with sound from a particular sound source from other sound sources in the local area based on, e.g., different DOA estimates from the DOA estimation module  240  and the tracking module  260 . The beamforming module  270  may thus selectively analyze discrete sound sources in the local area. In some embodiments, the beamforming module  270  may enhance a signal from a sound source. For example, the beamforming module  270  may apply sound filters which eliminate signals above, below, or between certain frequencies. Signal enhancement acts to enhance sounds associated with a given identified sound source relative to other sounds detected by the sensor array  220 . 
     The sound filter module  280  determines sound filters for the transducer array  210 . In some embodiments, the sound filters cause the audio content to be spatialized, such that the audio content appears to originate from a target region. The sound filter module  280  may use HRTFs and/or acoustic parameters to generate the sound filters. The acoustic parameters describe acoustic properties of the local area. The acoustic parameters may include, e.g., a reverberation time, a reverberation level, a room impulse response, etc. In some embodiments, the sound filter module  280  calculates one or more of the acoustic parameters. In some embodiments, the sound filter module  280  requests the acoustic parameters from a mapping server (e.g., as described below with regard to  FIG. 16 ). 
     The sound filter module  280  provides the sound filters to the transducer array  210 . In some embodiments, the sound filters may cause positive or negative amplification of sounds as a function of frequency. In some embodiments, audio content presented by the transducer array  210  is multi-channel spatialized audio. Spatialized audio content is audio content that appears to originate from a particular direction and/or target region (e.g., an object in the local area and/or a virtual object). For example, spatialized audio content can make it appear that sound is originating from a virtual singer across a room from a user of the audio system  200 . 
     In some embodiments, the audio system  200  utilizes a TPARN for multichannel speech enhancement in time-domain. In some other embodiments, the audio system  200  performs digital signal processing by distributing computation of a digital signal processing algorithm among slow and fast digital signal processing paths. In yet some other embodiments, the transducer array  210  of the audio system  200  includes an array of ultrasound transducers for generating virtual sounds source(s) to emulate haptic feedback to a user of the audio system  200 . 
     Ultrasound Speakers in Artificial Reality Headsets 
       FIG. 3A  illustrates an example presentation  300  of near ear virtual sounds to a user of an audio system, in accordance with one or more embodiments. In artificial reality applications, it is often desirable to create one or more virtual sounds to convince the user that the one or more sound sources are near a user&#39;s ear. As shown in  FIG. 3A , a near virtual sound  305 A can be presented to a right user&#39;s ear, and a near virtual sound  305 B can be presented to a left user&#39;s ear. There are certain challenges for achieve desired near-sound effects, especially for an open ear fitting of an eyewear device (headset) worn by the user, as the sound source is typically located in a temple arm of the eyewear device. 
       FIG. 3B  illustrates an example usage  310  of phased array (PA) speakers  315 A,  315 B for generating haptics and audio output, in accordance with one or more embodiments. Each PA speaker  315 A,  315 B may generate an ultrasound converging to a dedicated location to cause air to vibrate such that to create haptics sensation of a nearby virtual sound source. The PA speaker  315 A may generate a first ultrasound that creates haptics sensation of a nearby virtual sound source  320 A. Similarly, the PA speaker  315 B may generate a second ultrasound that creates haptics sensation of a nearby virtual sound source  320 B. The virtual sound sources  320 A,  320 B may be generated by the converging air molecule vibrations from the PA speakers  315 A,  315 B. For example, an audio output may focus on the broadband 20 Hz to 20 kHz in 40 dB-140 dB sound pressure level (SPL) output. Each PA speaker  315 A,  315 B may be implemented as an ultrasound PA of speakers. Furthermore, each PA speaker  315 A,  315 B may be mounted on a on a headset (not shown in  FIG. 3B ). The PA speakers  315 A,  315 B may be an embodiment of the transducer array  210 . 
     Ultrasonic micromachined transducers, including a piezoelectric micromachined ultrasonic transducer (PMUT) and a capacitive micromachined ultrasonic transducer (CMUT), have been widely used for fingerprint sensing, gesture recognition, and portable medical ultrasound systems. PMUTs and CMUTs usually operate in the ultrasonic frequency range, e.g., in the frequency range greater than 20 kHz. Some embodiments of the present disclosure are directed to small sized PMUTs and CMUTs that operating towards the audio frequency output (e.g., 20 Hz to 20 kHz). 
     The CMUT and PMUT transducers presented herein are utilized to produce audio-band frequency output by modulating audio signals into ultrasonic carrier signals. Both CMUT and PMUT transducers have resonances in ultrasonic frequency ranges. Both CMUT and PMUT transducers have an array of small transducer diaphragms on the same chip. Through modulation, the ultrasound signals can be converted into the audio frequency output. By operating in the ultrasonic frequency range, substantial savings on the size and weight of the transducer can be achieved. Additionally, acoustic leakage/privacy issues can be efficiently mitigated by utilizing the CMUT and/or PMUT transducers operating in the ultrasonic frequency range. Also, vibrations that penetrate a headset and thus causing problems for accurate operations of IMU and other optic components may be also mitigated by utilizing the CMUT and/or PMUT transducers operating in the ultrasonic frequency range. 
       FIG. 4  illustrates an example top view of a CMUT  400 , in accordance with one or more embodiments. As shown in  FIG. 4 , the CMUT  400  includes an array of transducer diaphragms  405  positioned on the same chip. The CMUT  400  may generate an audio-band output by modulating audio content into ultrasonic carrier signals. The CMUT  400  may be integrate and utilized within an audio system (e.g., the audio system  200 ) mounted on a headset (e.g., smart eyeglasses) with a smart form factor. The CMUT  400  may be an embodiment of a transducer in the transducer array  210 . 
       FIG. 5A  illustrates an example topology of a PMUT  500 , in accordance with one or more embodiments.  FIG. 5B  illustrates an example array of PMUTs  510 , in accordance with one or more embodiments. For example, the array of PMUTs  510  may be an array of 110×56 PMUTs. The array of PMUTs  510  may be an embodiment of the transducer array  210 . 
       FIG. 5C  illustrates an example cross-section of a PMUT  520 , in accordance with one or more embodiments. The PMUT  520  includes a top electrode  525  implemented as, e.g., a piezoelectric conductive sensing layer coupled to a first surface of the PMUT  520 . The PMUT  520  further includes a bottom electrode  530  implemented as, e.g., a piezoelectric conductive sensing layer coupled to a second surface of the PMUT  520  opposite to the first surface. Outputs of the first and second conductive sensing layers (e.g., outputs of the top and bottom electrodes  525 ,  530 ) form output information (e.g., charge or voltage) responsive to a stress induced in the PMUT  520  by displacement of one or more moving components of the PMUT  520 . The PMUT  520  may be an embodiment of a transducer in the transducer array  210 . 
       FIG. 5D  illustrates an example top view of a PMUT array  540 , in accordance with one or more embodiments. The PMUT array  540  includes an array of PMUTs  541 , and each PMUT  541  in the PMUT array  540  may be of, e.g., a square or rectangular shape. Each PMUT  541  in the PMUT array  540  includes an active area  542  surrounding an area of a bottom electrode  544 . The PMUT array  540  further includes an anchor  546  and a eutectic bonding  548 . Separation between two adjacent PMUTs  541  in the PMUT array  540  may be, e.g., less than 30 μm. The PMUT array  540  may be an embodiment of the transducer array  210 . 
       FIG. 5E  illustrates an example top view of a PMUT array  550 , in accordance with one or more embodiments. The PMUT array  550  includes an array of PMUTs  551 , and each PMUT  551  in the PMUT array  550  may be of an oval (e.g., circular) shape. Each PMUT  551  in the PMUT array  550  includes an active area  552  surrounding an area of a bottom electrode  554 . A width of the bottom electrode  554  may be, e.g., less than 30 μm. The PMUT array  550  may further include an anchor  556 . Each PMUT  551  in the PMUT array  550  may also include a eutectic bonding  558  implemented as a ring surrounding the bottom electrode  554 . The PMUT array  550  may be an embodiment of the transducer array  210 . 
     Some embodiments of the present disclosure relate to different methods of integrating and utilizing the presented CMUT and PMUT transducers for a smart eyeglasses with a small form factor. In some embodiments, the CMUT and/or PMUT transducers are utilized in conjunction with a waveguide for achieving flexibility of transducer placement. Given tight space budget on a smart eyeglasses, it is not always possible to place a CMUT and/or PMUT transducer at a most desired location for acoustic radiation efficiency. However, the CMUT and/or PMUT transducer(s) may be at any locations that are physically allowed on the smart eyeglasses and include one or more ultrasound waveguides (e.g., horn, duct, or metamaterial waveguide) guiding the sound to the ideal porting locations to increase the transduction efficiency. Additionally, impedance matching membranes or material may be used at the porting location for optimal acoustic radiation efficiency. 
     In some other embodiments, instead of using a single CMUT and/or PMUT transducer element, an array of such CMUT and/or PMUT transducers can be employed to operate together to improve the performance. For example, a number of such CMUT and/or PMUT transducers can be placed along arms of a smart eyeglasses with uniform or nonuniform spacing to form a linear transducer array. Certain beamforming techniques can be applied to control the directionality of the radiation pattern so that a sound level at an ear canal entrance can be at a preferred level. An array of CMUT and/or PMUT transducers can also be used to detect the motion gesture for a new user interface. An array of CMUT and/or PMUT transducers can also be used for implementing an imaging technique to detect an ear shape for a personalized sound equalization. This can also provide direction information for the transducer array processing to fine tune the radiation pattern for each wearer of smart eyeglasses. 
     The CMUT and PMUT transducers presented herein may also employ cartilage or bone conduction pathway. With much higher energy penetration of ultrasound, cartilage and/or bone conduction pathways can be leveraged to directly excite an ear-drum or cochlear. This approach may require: (i) tuning of carrier frequencies for more efficient conduction in human tissue, and (ii) design of impedance matching material interfacing between a transducer and human skin. 
     Multi-Channel Speech Enhancement using Triple-Path Attentive Recurrent Network 
     Embodiments of the present disclosure are further directed to a triple-path attentive recurrent network (TPARN) for ad-hoc array processing that can be applied at an audio system (e.g., the audio system  200 ) for multi-channel speech enhancement. A TPARN&#39;s dual-path single-channel network may be extended to a multichannel network by adding a third path along a spatial dimension. The self-attention along the spatial dimension is presented herein for ad-hoc arrays to make the TPARN invariant to a number and order of microphones. The TPARN presented herein may improve speech enhancement by placing additional microphones at new locations, even at locations far from existing microphones. The TPARN presented herein may simultaneously enhance signals from all microphones. Additionally, worse-placed microphones at far locations may exhibit biggest improvement in objective scores by utilizing better-placed microphones at closer locations. The TPARN presented herein can be very effective in highly reverberant and noisy conditions. The TPARN for ad-hoc array processing is extended herein by incorporating two major changes. First, using self-attention across the spatial dimension instead of an attentive recurrent network (ARN), the TPARN is made invariant to the number and the order of microphones. Second, a processing order of underlying TPARN processing blocks (e.g., intra-chunk, inter-chunk, and inter-channel blocks) is rearranged based on empirical observations. 
     A multichannel noisy signal Y∈R P×N , where P is the number of microphones and N is the number of samples, can be modeled as Y=X+N, where X represents a clean speech and N represents noises including background noise and room reverberation recorded at P microphones. Let X i  denote the signal at the i-th microphone. The goal of multichannel speech enhancement is to obtain a close estimate of the clean signal at a reference microphone r, {circumflex over (X)} r , from Y. 
       FIG. 6  is a block diagram of a TPARN  600  for an ad-hoc array multichannel speech enhancement, in accordance with one or more embodiments. The TPARN  600  represents a multiple-input multiple-output (MIMO) processing architecture that enhances all input channels simultaneously. The TPARN  600  may include: a frame conversion block  605 , a chunk conversion block  610 , an input linear layer  615 , TPARN blocks  620 ( 1 ),  620 ( 2 ),  620 ( 3 ),  620 ( 4 ), an output linear layer  625 , a chunk overlap-and-add (OLA) block  630 , and a frame OLA block  635 . There may be more or fewer components of the TPARN  600  than what is shown in  FIG. 6 . The TPARN  600  may be implemented as part of one or more modules of the audio controller  230 . 
     The frame conversion block  605  may convert an input signal comprising a multichannel noisy speech  602  to frames (e.g., T frames each having a size of L samples and frame shift of K samples). The chunk conversion block  610  may group the frames into chunks (e.g., C chunks each having a size of R samples and chunk shift of S samples). The input linear layer  615  may project the frames grouped into the chunks to a size of D samples. The stack of TPARN blocks  620 ( 1 ),  620 ( 2 ),  620 ( 3 ),  620 ( 4 ) may process the projected frames output by the input linear layer  615 . 
     An input to a TPARN block  620 ( 1 ),  620 ( 2 ),  620 ( 3 ),  620 ( 4 ) (which is a concatenation of the output from the encoder and outputs from preceding TPARN blocks), is a tensor of shape P×C×R×k·D, where k denotes an identifier of the TPARN block (i.e., k=1, 2, 3, or 4). For k&gt;1, a linear layer may be used at an input of each TPARN block  620 ( 2 ),  620 ( 3 ),  620 ( 4 ) to project features of size k·D samples to D samples. After that, the projected features may be processed using a stack of inter-channel self-attention block, intra-chunk ARN, and inter-chunk ARN within each TPARN block  620 ( 2 ),  620 ( 3 ),  620 ( 4 ). The inter-channel self-attention block may operate along the channel dimension (i.e., spatial dimension) by rearranging its input to shape R·C×P×D and using a self-attention mechanism that treats the first, second and the third dimensions as batch, sequence, and feature dimensions, respectively. Similarly, the intra-chunk ARN may process all chunks independently by rearranging its input to shape P·C×R× D. The inter-chunk ARN may combine chunks by rearranging its input to shape R·C×P×D. 
     The output linear layer  625  may project an output of the TPARN block  620 ( 4 ) to size L samples. The chunk OLA block  630  may combine chunks of samples from the output linear layer  625 . The frame OLA block  635  may combine frames from the chunk OLA block  630  to generate an enhanced multichannel waveform comprising a multichannel enhanced speech. 
       FIG. 7  is a block diagram of a TPARN block  700 , in accordance with one or more embodiments. The TPARN block  700  may be an embodiment of any of the TPARN blocks  620 ( 1 ),  620 ( 2 ),  620 ( 3 ),  620 ( 4 ). The TPARN block  700  may include: a linear layer  705 , a rearrange dimensions block  710 , an inter-channel attention block  715 , a rearrange dimensions block  720 , an intra-chunk ARN  725 , a rearrange dimensions block  730 , an inter-chunk ARN  735 , and a rearrange dimensions block  740 . There may be more or fewer components of the TPARN block  700  than what is shown in  FIG. 7 . 
     The inter-channel attention block  715  may include a stack of an attention block  745  and a feedforward block  750 . The inter-channel attention block  715  may operate along the channel dimension (i.e., spatial dimension) by rearranging its input to a defined shape (e.g., R·C×P×D shape) and employ a self-attention mechanism that treats the first, second and the third dimensions as batch, sequence, and feature dimensions, respectively. Each of the intra-chunk ARN  725  and the inter-chunk ARN  735  may include a respective stack of three blocks, a respective recurrent neural network (RNN) block  755 , a respective attention block  760 , and a respective feedforward block  765 . The intra-chunk ARN  725  may process all chunks independently by rearranging its input to a specific shape (e.g., P·C×R×D shape). The inter-chunk ARN  735  may combine chunks by rearranging its input to a specific shape (e.g., R·C×P×D shape). 
       FIG. 8A  is a block diagram of a RNN block  800 , in accordance with one or more embodiments. The RNN block  800  may be an embodiment of the RNN block  755 . The RNN block  800  may include: a layer-normalization layer  805 , a layer-normalization layer  810 , a RNN  815 , a concatenation block  820 , and a linear layer  825 . There may be more or fewer components of the RNN block  800  than what is shown in  FIG. 8A . 
     The two independent layer-normalization layers  805  and  810  may normalize an input into the RNN block  755  (of hidden size D). A first layer-normalized input generated by the layer-normalization layer  805  may be fed into an RNN  815  of hidden size 2D. The concatenation block  820  may concatenate an output of the RNN  815  with a second layer-normalized input generated by the layer-normalization layer  810  to generate a concatenated output of hidden size 3D. The linear layer  825  may project the concatenated output to an output of the RNN block  800  of size D. 
       FIG. 8B  is a block diagram of an attention block  830 , in accordance with one or more embodiments. The attention block  830  may be an embodiment of the attention block  745  or the attention block  760 . The attention block  830  may include a layer-normalization layer  835 , a layer-normalization layer  840 , and an attention module  845 . There may be more or fewer components of the attention block  830  than what is shown in  FIG. 8B . 
     An input to the attention block  830  may be layer-normalized using the two independent layer-normalization layers  835 ,  840 . A first layer-normalized input generated by the layer-normalization layer  835  may be used as query, Q. A second layer-normalized input generated by the layer-normalization layer  840  may be used as key K and value V for the attention module  845 . The attention module  845  may use a self-attention mechanism to process the query, Q, key, K, and value, V. An output of the attention module  845  may be added to the second layer-normalized input generated by the layer-normalization layer  840  to form a residual connection. 
       FIG. 8C  is a block diagram of a feedforward block  850 , in accordance with one or more embodiments. The feedforward block  850  may be an embodiment of the feedforward block  750  or the feedforward block  765 . The feedforward block  850  may include: a layer-normalization layer  855 , a layer-normalization layer  857 , a linear layer  860 , a Gaussian error linear unit (GELU)  865 , a dropout block  870  and a linear layer  875 . There may be more or fewer components of the feedforward block  850  than what is shown in  FIG. 8C . 
     An input to the feedforward block  850  may be layer-normalized independently using the two different layer-normalization layers  855 ,  857 . The linear layer  860  may project a first layer-normalized input generated by the layer-normalization layer  855  to generate a projected input of size 4D. The GELU  865  may apply a nonlinear processing to the projected input to generate a nonlinear output. The dropout block  870  may apply dropout (e.g., with a dropout rate of Dr) to samples of the nonlinear output. The linear layer  875  may project an output of the dropout block  870  back to a projected output of size D samples. The projected output may be added to a second layer-normalized input generated by the layer-normalization layer  857  to generate an output of the feedforward block  850 . It should be noted that that dense connections can be used in the RNN block  800 , whereas residual connections can be used in the attention block  830  and the feedforward block  850 . 
     Sound Enhancement using Triple-Path Attentive Recurrent Network 
     Deep neural networks (DNNs) are generally coupled with one or more spatial filters (e.g., MVDR beamformer) for exploiting spatial information. Single-stage end-to-end supervised models can provide substantial sound enhancement. Moreover, utilizing the end-to-end supervised models in a multistage scheme with a beamformer and a DNN-based post-filter may provide additional improvements in sound enhancement. Embodiments of the present disclosure are directed to a two-stage scheme for multichannel speech enhancement that does not require a beamformer. A novel attentive dense convolutional network (ADCN) for multichannel complex spectral mapping (CSM) is presented herein. The ADCN is an encoder-decoder based convolutional neural network (CNN) architecture where layers within the encoder and decoder are augmented with dense blocks and attention blocks for context aggregation. Furthermore, different models are evaluated in a two-stage scheme with and without an MVDR beamformer. Two CSM models are evaluated herein: (i) ADCN and a dense convolutional recurrent network (DCRN), and one waveform mapping (WM) model TPARN. Improvements due to the MVDR beamformer can be observed only when similar approaches to speech enhancement, such as CSM are used in both stages. It can be also noticed that the improvements from the MVDR beamformer diminishes with a stronger model in the first stage. For example, with stronger models (e.g., ADCN and TPARN), consistent or significant improvements from the MVDR beamformer cannot be observed. 
     A multichannel noisy speech x=[x 1 , . . . , x p ]∈R P×N  with N samples and P microphones can be modeled as: 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
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     where m=1, 2, . . . , P, n=0, 1, . . . , N−1, s is the source speech, y m  and z m  are respectively the reverberated speech and the noise received at the m-th microphone, * denotes convolution operator, h m  is a room impulse response (RIR) of the source speech, g m  is a direct-path RIR of the source speech that is ideally a scaled and shifted delta operator to incorporate for the delay and the attenuation in the arrival of the source speech, d m  is a direct-path source speech, and u m  is an overall interference including noise and reverberation. Given a reference microphone r, the goal of a multichannel speech enhancement algorithm is to obtain a good estimate of the direct-path source speech at microphone r, {circle around (d)} r , from the multichannel noisy speech x. 
       FIG. 9  is a block diagram of an ADCN  900  for multichannel speech enhancement, in accordance with one or more embodiments. The ADCN  900  may perform the CSM based processing to achieve multichannel speech enhancement. The ADCN  900  comprises a CNN architecture with an encoder  905  and a decoder  910 . The ADCN  900  further incudes a Short-time Fourier transform (STFT) block  912  coupled to an input of the encoder  905 , and an inverse Short-time Fourier transform (iSTFT) block  915  coupled to an output of the decoder  910 . There may be more or fewer components of the ADCN  900  than what is shown in  FIG. 9 . The ADCN  900  may be implemented as part of one or more modules of the audio controller  230 . 
     The STFT block  912  may perform a STFT on time domain input samples of a multichannel noisy speech  902  to generate a processed input in frequency domain for the encoder  905  having a shape of, e.g., 2·P×T×257. A first portion of the encoder (e.g., 5×5 convolutional layer with layer normalization (LN) and parametric rectified linear unit (PReLU)) may transform the processed input into a transformed signal having a shaped of, e.g., C×T×257. Following that, a second portion of the encoder  905  (e.g., last six blocks in the encoder  905  shown in  FIG. 9 ) and a first portion of the decoder  910  (e.g., first six blocks in the decoder  910  in  FIG. 9 ) may process the transformed signal to generate a processed output of, e.g., 2×T×257 shape. The encoder  905  may comprises a stack of a dense block, a 1×3 convolutional block (e.g., using a stride of 2 for down-sampling) with LN and PReLU, and an attention block. 
     An output of the attention block in the encoder  905  may be concatenated with its input to generate a final output of the encoder  905 . Additionally, an output of each block in the decoder  910  may be concatenated to an output of a corresponding symmetric block in the encoder  905 . A second portion of the decoder  910  (e.g., 5×5 convolutional layer with two output channels) may generate a final output of the decoder  910  of, e.g.,  1 ×N shape. The iSTFT block  915  may perform an inverse STFT to samples of the final output of the decoder to generate a time domain output waveform comprising an enhanced speech  920 . Note that the decoder  910  may have a similar structure as the encoder  905 , except that the decoder uses 1×3 sub-pixel convolution for up-sampling instead of strided convolution for down-sampling applied at the encoder  905 . 
       FIG. 10A  is a block diagram of a dense block  1000  of the ADCN  900 , in accordance with one or more embodiments. The dense block  1000  may comprises a stack of five 3×3 convolutional layer blocks with C output channels, LN and PReLU. There may be more or fewer components of the dense block  1000  than what is shown in  FIG. 10 . An input to each convolutional layer block in the dense block  1000  may be a concatenation of a respective input to each convolutional layer block and outputs from preceding convolutional layers in the dense block  1000 . 
       FIG. 10B  is a block diagram an attention block  1020  of the ADCN  900 , in accordance with one or more embodiments. An input of shape C×T×L may be first transformed using three separate 1×1 convolutional layers to obtain a query Q, key K, and value V of shapes E×T×L, E×T×L, and J×T×L, respectively. Then, the query Q, key K, and value V may be reshaped to two-dimensional tensors of shapes E·L×T, E·L×T, and J·L×T, respectively. After that, an output from attention, A, may be computed as A=Softmax(Q×K T )×V and having a shaped of J·L×T. Finally, the output A may be reshaped to a three-dimensional tensor of shape J×T×L. 
     Embodiments of the present disclosure further relate to evaluating DCRN, ADCN and TPARN models using two stages for multichannel speech enhancement. 
       FIG. 11A  is a block diagram of a two-stage sound-enhancement architecture  1100  with a MVDR beamformer, in accordance with one or more embodiments. The two-stage sound-enhancement architecture  1100  may include a model  1105 , a MVDR beamformer  1110 , and a model  1115 . There may be more or fewer components of the two-stage sound-enhancement architecture  1100  than what is shown in  FIG. 11A . The two-stage sound-enhancement architecture  1100  may be implemented as part of one or more modules of the audio controller  230 . 
     The model  1105  may be first trained to estimate enhanced speech at all channels. The model  1105  may be a TPARN model (i.e., MIMO model) model. The model  1105  may process input samples of a multichannel noise speech  1102  to generate a multichannel enhanced signal  1107 . The MVDR beamformer  1110  may estimate beamformer coefficients using the multichannel enhanced signal  1107  at all channels. The MVDR beamformer  1110  may be implemented as a time-invariant MVDR (TI-MVDR) beamformer. A sound source used herein can be considered static within each utterance, and reverberation and background noise are considered to be diffuse. Hence, the MVDR beamformer  1110  implemented as a TI-MVDR is more suitable herein than a time-varying MVDR beamformer. The MVDR beamformer  1110  may use the estimated beamformer coefficients to generate a single channel enhanced signal  1112  from the multichannel enhanced signal  1107 . 
     The model  1115  may be trained to map the multichannel noise speech  1102  and the enhanced signal  1112  from the MVDR beamformer  1110  to an enhanced speech  1117 . The model  1115  may include a DCRN and ADCN, i.e., the model  1115  may be a multiple-input and single-output (MISO) model. Hence, the model  1115  may enhance each channel independently by running the enhancement model P times for P channels. An output for the m-th microphone may be computer at the model  1115  by using a circularly shifted input. This is suitable as a symmetric circular microphone array is used. 
       FIG. 11B  is a block diagram of a two-stage sound-enhancement architecture  1120  without a MVDR beamformer, in accordance with one or more embodiments. The two-stage sound-enhancement architecture  1120  may include a model  1125 , and a model  1130 . There may be more or fewer components of the two-stage sound-enhancement architecture  1120  than what is shown in  FIG. 11B . The two-stage sound-enhancement architecture  1120  may be implemented as part of one or more modules of the audio controller  230 . 
     The model  1125  may be first trained to estimate enhanced speech at all channels. The model  1125  may be a TPARN model (i.e., MIMO model) model. The model  1125  may process input samples of a multichannel noise speech  1122  to generate a multichannel enhanced signal  1127 . The model  1130  may be trained to map the multichannel noise speech  1122  and the multichannel enhanced signal  1127  to an enhanced speech  1135  at a reference channel. The model  1130  may include a DCRN and ADCN, i.e., the model  1130  may be a MISO model. Hence, the model  1130  may enhance each channel independently by running the enhancement model P times for P channels. 
     Low Latency Low Power DSP Control Architecture 
     An architecture with low latency and low power digital signal processing and control is presented herein. The architecture is based on distributing a computation of digital signal processing algorithms among slow and fast processing paths. A portion of the algorithm dependent on more recent information may be executed on the fast processing path, and another portion of the algorithm dependent on older information may be executed on the slow processing path. Outputs of each processing path may be combined to generate a target algorithm output in a timely manner. The digital signal processing architecture presented herein reduces latency associated with transferring samples out of analog-to-digital converters (ADCs) and into digital-to-analog converters (DACs). It should be noted that the discrete convolution implementing a finite impulse response (FIR) filter discussed below is one example of digital signal processing and should not be limiting. The architecture presented herein may also reduce a number of bits used to represent digitized samples that are being processed at high rates such as in, e.g., active noise cancellation (ANC) applications. Thereby, the presented approach results in various system level benefits due to the achievement of reduced power consumption, reduced memory and reduced latency. The architecture presented herein is also designed to meet system level requirements including low latency, low power consumption and multi-chip implementation. 
     Some embodiments of the present disclosure relate to a method that divides a set of samples (e.g., from an ADC) into a first set of samples and a second set of samples, wherein the first set of samples are associated with a more recent set of time values than the second set of samples. The first set of samples may be processed using a first digital signal processor (DSP), and the second set of samples may be processed using a second DSP. The second DSP may be slower than the first DSP. Outputs from the first DSP and the second DSP may be then combined to form a combined output. The combined output may be representative of a filtering operation being applied to the set of samples. The combined output may then be provided to, e.g., a DAC. Use of the first DSP and the second DSP can reduce latency associated with transferring samples out of ADCs and into DACs. 
     An amount of time, TDSP, can be attributed to a common type of filter (e.g., FIR filter) used by DSP applications. Mathematically, the FIR filter can be described by the following difference equation (also known as the discrete convolution): 
         y [ k ]=Σ i=0   N   bi·x [ k−i ]  (2)
 
     where N is a filter order, x[k] is an input sample at instant k*Ts, y[k] is an output sample at instant k*Ts, and bi is an i-th filter coefficient multiplying [k−i]-th input sample. 
     As the order N of the filter increases, so does the amount of memory required to store all input samples and filter coefficients, as well as an amount of processor cycles to finish the convolution operation. Therefore, with a separated architecture, satisfying latency constraints can be particularly difficult for high sampling rates. An architecture and method is presented herein to allow DSP and control systems having large number of computations (i.e., high TDSP) and relatively high sample transfer delays Tdly to be executed at small sampling periods while keeping the overall input-output latency below a defined threshold latency. 
       FIG. 12A  illustrates an example architecture  1200  for splitting the convolution calculation between two DSPs, in accordance with one or more embodiments. The architecture  1200  may include ADC  1202 , DSP  1205 , DSP  1210 , and DAC  1215 . There may be more or fewer components of the architecture  1200  than what is shown in  FIG. 12A . The architecture  1200  may be implemented as part of one or more modules of the audio controller  230 . 
     Note that Equation (2) can be re-written as an addition operation between two convolutions defined as: 
         y [ k ]= y 1[ k ]+ y 2[ k−n ]  (3)
 
         y 1[ k ]=Σ i=0   n-1   bi·x [ k−i ]  (4)
 
         y 2[ k−n ]=Σ i=n   N   bi·x [ k−i ]  (5)
 
     where n−1 is a filter order of FIR filter implemented on a DSP  1205 , and N is a total filter order (i.e., filter order of a cumulative FIR filter implemented at the DSP  1205  and the DSP  1210 ). If the choice of where n splits the FIR filter is properly selected to account for sources of delay T dly , then the architecture  1200  may guarantee that a large convolution is executed in a very short amount of sampling time Ts without being delayed by the amount of time it takes for the samples to move through the large DSP  1210 . In this way, the DSP  1205  may be a much smaller and faster processor belonging to a Codec that also includes the ADC  1202  and the DAC  1215 . 
       FIG. 12B  illustrates an example timing diagram  1230  for the convolution split, in accordance with one or more embodiments. The convolution split may be at n−4 and transfer delays may be 2*Ts. It can be observed from the timing diagram  1230  that n≥Tdly_total/Ts is chosen so that the result y2[k−n] of convolution performed at the DSP  1210  arrives in time to be added to the result y1[k] of convolution performed at the DSP  1205 , thus yielding y[k] that is sent to the DAC  1220 . It should be noted that the choice of 2 DSP cores (or co-processors)  1205 ,  1210  is merely exemplary and by any means should it limit the extension to multiple cores and/or multiple convolutions (i.e., to more than two DSPs and more than two convolutions). It should be noted that by keeping n small, an amount of memory and computation performed by the DSP  1205  is reduced and therefore a design of the DSP  1205  may be more easily optimized for low input-output latency and low sampling time Ts. In some embodiments, n may be increased to allow longer transfer delays of the x samples and y2 samples (e.g., through serial audio interfaces), and thus keep a clock rate from becoming higher than a defined threshold rate. 
       FIG. 13  illustrates an example block diagram  1300  of a coefficient bank switching control, in accordance with one or more embodiments. The filter coefficients may be adapted to modify the FIR filter characteristics, e.g., at an adaptation rate lower than the sampling rate Fs. A method is presented herein to change (adapt) the filter coefficients by writing new filter coefficients to shadow memory banks not actively used by the FIR filter. Upon writing all the filter coefficients, a bank selection command  1302  may be sent out synchronously to both DSPs  1305 ,  1310  in order to atomically activate the banks containing the filter coefficients. A controller  1315  coupled to the DSPs  1305 ,  1310  may run a least mean square (LMS) algorithm responsible for generating the modified filter coefficients. The approach presented herein can also be applied to the case where independent DSPs share a common physical memory containing all coefficient banks. 
     In some embodiments, an additional enhancement to the architecture  1200  is achieved by reducing the number of bits used to represent samples required for the convolution operation. One benefit of the additional enhancement would be lowering an amount of memory for storing the array (or arrays) of samples. Another benefit of the additional enhancement would be reducing the power and silicon resources dedicated to implementing the multiply operations bi*x[k−i] of the convolution, for instance by reducing the multiply operations from 32×32 bits to 32×16, 32×8, 24×8, etc. Finally, it takes less time to transfer input and output samples x[k] and y[k] between the DSPs, lowering latency and/or reducing the requirement for high communication clock frequency. For applications such as headphone ANC, keeping Ts small helps achieving better performance and/or stability margins and therefore prevents oscillations that cause the speaker to “squeal” at times. The use of smaller Ts (or equivalently higher sampling frequency Fs) may cause the ANC algorithm to operate at a sampling rate that is higher than a typical Nyquist rate (e.g., Fn=24 kHz) of a consumer quality audio playback application, therefore creating an opportunity for x[k] samples to be quantized with a lower number of bits while maintaining the quantization noise outside the listening band of interest (e.g., Fn). A few sampling rates considered for running the ANC algorithms are listed in Table I, with the corresponding oversampling rates (OSR) calculated as defined. 
     
       
         
           
               
             
               
                 TABLE I 
               
             
            
               
                   
               
               
                 Oversampling rates for various 
               
               
                 ANC sampling frequencies. 
               
            
           
           
               
               
               
            
               
                   
                 Fs 
                 OSR = 
               
               
                   
                 [kHz] 
                 Fs/(2*Fn) 
               
               
                   
                   
               
               
                   
                  48 
                  1 
               
               
                   
                  96 
                  2 
               
               
                   
                 192 
                  4 
               
               
                   
                 384 
                  8 
               
               
                   
                 768 
                 16 
               
               
                   
                   
               
            
           
         
       
     
       FIG. 14A  illustrates an example signal processing architecture  1400  with a quantizer for splitting a convolution calculation between two DSP paths, in accordance with one or more embodiments. The signal processing architecture  1400  may include an ADC  1402 , a quantizer  1405 , a DSP  1410 , a DSP  1415 , and a DAC  1417 . There may be more or fewer components of the signal processing architecture  1400  than what is shown in  FIG. 14A . The signal processing architecture  1400  may be implemented as part of one or more modules of the audio controller  230 . 
     The quantizer  1405  may quantize samples from the ADC  1402 , and the quantized samples may be then split to the DSP  1410  and the DSP  1415  for two separate convolutions. Outputs of the DSPs  1410 ,  1415  may be then combined and fed into the DAC  1417 . The quantizer  1405  may facilitate reducing the number of bits of the input samples coming from the ADC  1402 . 
     During the quantization process performed at the quantizer  1405 , noise that can be detrimental to a pleasant listening experience may be added to the continuous signal sampled by the ADC  1402 . A technique used herein is to move that noise out of the audible band into the higher inaudible range of the frequency spectrum, which can be referred to as “noise shaping.” In general, the higher the oversampling rate the lower the number of bits that the quantization process can utilize while maintaining acceptable levels of noise within the audible range. 
     One immediate solution to keep the quantization noise from entering the audible range is to increase the sampling rate Fs from, e.g., 192 kHz to 384 kHz or higher. However, such a solution happens at the expense of incurring higher power consumption due to increased computation rate. To maintain the same Fs=192 kHz and low 8 bits, nonlinear quantization using μ-law as shown in  FIG. 14B  can be used. 
       FIG. 14B  illustrates an example signal processing architecture  1420  with a nonlinear quantization for splitting a convolution calculation between two DSP paths, in accordance with one or more embodiments. The signal processing architecture  1420  may include an ADC  1422 , a μ-law compressor  1425 , a quantizer  1430 , a μ-law expander  1435 , a DSP  1440 , a DSP  1445 , and a DAC  1450 . There may be more or fewer components of the signal processing architecture  1420  than what is shown in  FIG. 14B . The signal processing architecture  1420  may be implemented as part of one or more modules of the audio controller  230 . 
     The μ-law compressor  1425 , the quantizer  1430  and the μ-law expander  1435  may perform the nonlinear quantization. Alternatively or additionally, a spectrum of the quantization noise may be shaped at the-law compressor  1425 , the quantizer  1430  and the μ-law expander  1435  such that the quantization noise becomes minimally audible or provide the preferred listening experience in the psychoacoustic sense. For example, with placement of a notch at 13 kHz, there is a small increase of the noise from in the 1 kHz to 10 KHz range but reduction in the 10 kHz to 20 kHz range. 
     The noise energy moved out of the audible range may appears at higher frequencies, which could negatively impact certain performance characteristics of the system. For example, if the quantized signal is sent to an audio amplifier connected to a loudspeaker load, excessive power dissipation may occur depending on its impedance characteristics. It is common to find loudspeakers (i.e., receivers) having high impedance and therefore low power dissipation due to quantization noise. Such receivers may be of the balanced armature type and typically unable to generate enough sound pressure at low frequencies to meet bass levels normally expected from consumer audio products. On the other hand, loudspeakers of the electrodynamic type typically produce more bass response but dissipate more of the quantization noise power due to their lower impedance characteristic. To prevent quantization noise from leaving the digital domain, a low-pass filter can be cascaded after the noise shaping block. 
     A method is presented herein for distributing a computation of an algorithm among slow and fast processing paths. A set of signal samples may be divided into a first set of samples and a second set of samples, wherein the first set of samples are associated with a more recent set of time values than the second set of samples. The first set of samples is processed using a first DSP of the headset. The second set of samples is processed using a second DSP of the headset, wherein the second DSP is slower than the first DSP. Outputs from the first DSP and the second DSP are combined to form a combined output for presentation to the user, the combined output representative of a filtering operation being applied to the set of signal samples. 
     Process Flow 
       FIG. 15  is a flowchart illustrating a process  1500  for multi-channel speech enhancement at an audio system, in accordance with one or more embodiments, in accordance with one or more embodiments. The process  1500  shown in  FIG. 15  may be performed by various components of the audio system (e.g., components shown in  FIG. 6 ,  FIG. 7 , and  FIGS. 8A-8C ). The audio system may be the audio system  200 . Other entities may perform some or all of the steps in  FIG. 15  in other embodiments (e.g., components shown in  FIG. 9 ,  FIGS. 10A-10B , and  FIGS. 11A-11B ). Embodiments may include different and/or additional steps, or perform the steps in different orders. 
     The audio system detects  1505  audio signals via an array of microphones. The array of microphones may be mounted on a headset that includes the audio system. 
     The audio system processes  1510  the detected audio signals using a DNN to generate enhanced audio content. The DNN may be trained using sounds detected by a subset of the microphones in the array. The subset of microphones may be mounted at random locations of the headset, and a number of the microphones in the subset may be randomly selected. In some embodiments, the audio system performs time-domain multichannel enhancement of the detected audio signals using a TPARN model of the DNN. A single-channel dual-path model may be extended to a multichannel model in the TPARN model by including a path along a spatial dimension for multichannel processing of the detected audio signals. In some other embodiments, the audio system processes the detected audio signals using an ADCN model of the DNN to generate a plurality of intermediate audio signals, and the audio system processes the intermediate audio signals using a TPARN model of the DNN coupled to the ADCN model to generate the enhanced audio content. 
     The audio system presents  1515  (e.g., via one or more transducers) the enhanced audio content to a user of the headset. In some embodiments, the one or more transducers are configured to output, in accordance with audio instructions, one or more ultrasonic pressure waves simulating a virtual audio source near an ear of the user. The one or more transducers may comprise an array of micromachined ultrasound transducers. The array of micromachined ultrasound transducers may comprises at least one of: one or more CMUTs, and one or more PMUTs. Alternatively, the one or more transducers may comprise a phased array of ultrasonic speakers. 
     System Environment 
       FIG. 16  is a system  1600  that includes a headset  1605 , in accordance with one or more embodiments. In some embodiments, the headset  1605  may be the headset  100  of  FIG. 1A  or the headset  105  of  FIG. 1B . The system  1600  may operate in an artificial reality environment (e.g., a virtual reality environment, an augmented reality environment, a mixed reality environment, or some combination thereof). The system  1600  shown by  FIG. 16  includes the headset  1605 , an input/output (I/O) interface  1610  that is coupled to a console  1615 , the network  1620 , and the mapping server  1625 . While  FIG. 16  shows an example system  1600  including one headset  1605  and one I/O interface  1610 , in other embodiments any number of these components may be included in the system  1600 . For example, there may be multiple headsets each having an associated I/O interface  1610 , with each headset and I/O interface  1610  communicating with the console  1615 . In alternative configurations, different and/or additional components may be included in the system  1600 . Additionally, functionality described in conjunction with one or more of the components shown in  FIG. 16  may be distributed among the components in a different manner than described in conjunction with  FIG. 16  in some embodiments. For example, some or all of the functionality of the console  1615  may be provided by the headset  1605 . 
     The headset  1605  includes a display assembly  1630 , an optics block  1635 , one or more position sensors  1640 , a DCA  1645 , and an audio system  1650 . Some embodiments of headset  1605  have different components than those described in conjunction with  FIG. 16 . Additionally, the functionality provided by various components described in conjunction with  FIG. 16  may be differently distributed among the components of the headset  1605  in other embodiments, or be captured in separate assemblies remote from the headset  1605 . 
     The display assembly  1630  displays content to the user in accordance with data received from the console  1615 . The display assembly  1630  displays the content using one or more display elements (e.g., the display elements  120 ). A display element may be, e.g., an electronic display. In various embodiments, the display assembly  1630  comprises a single display element or multiple display elements (e.g., a display for each eye of a user). Examples of an electronic display include: a liquid crystal display (LCD), an organic light emitting diode (OLED) display, an active-matrix organic light-emitting diode display (AMOLED), a waveguide display, some other display, or some combination thereof. Note in some embodiments, the display element  120  may also include some or all of the functionality of the optics block  1635 . 
     The optics block  1635  may magnify image light received from the electronic display, corrects optical errors associated with the image light, and presents the corrected image light to one or both eye boxes of the headset  1605 . In various embodiments, the optics block  1635  includes one or more optical elements. Example optical elements included in the optics block  1635  include: an aperture, a Fresnel lens, a convex lens, a concave lens, a filter, a reflecting surface, or any other suitable optical element that affects image light. Moreover, the optics block  1635  may include combinations of different optical elements. In some embodiments, one or more of the optical elements in the optics block  1635  may have one or more coatings, such as partially reflective or anti-reflective coatings. 
     Magnification and focusing of the image light by the optics block  1635  allows the electronic display to be physically smaller, weigh less, and consume less power than larger displays. Additionally, magnification may increase the field of view of the content presented by the electronic display. For example, the field of view of the displayed content is such that the displayed content is presented using almost all (e.g., approximately 110 degrees diagonal), and in some cases, all of the user&#39;s field of view. Additionally, in some embodiments, the amount of magnification may be adjusted by adding or removing optical elements. 
     In some embodiments, the optics block  1635  may be designed to correct one or more types of optical error. Examples of optical error include barrel or pincushion distortion, longitudinal chromatic aberrations, or transverse chromatic aberrations. Other types of optical errors may further include spherical aberrations, chromatic aberrations, or errors due to the lens field curvature, astigmatisms, or any other type of optical error. In some embodiments, content provided to the electronic display for display is pre-distorted, and the optics block  1635  corrects the distortion when it receives image light from the electronic display generated based on the content. 
     The position sensor  1640  is an electronic device that generates data indicating a position of the headset  1605 . The position sensor  1640  generates one or more measurement signals in response to motion of the headset  1605 . The position sensor  190  is an embodiment of the position sensor  1640 . Examples of a position sensor  1640  include: one or more IMUs, one or more accelerometers, one or more gyroscopes, one or more magnetometers, another suitable type of sensor that detects motion, or some combination thereof. The position sensor  1640  may include multiple accelerometers to measure translational motion (forward/back, up/down, left/right) and multiple gyroscopes to measure rotational motion (e.g., pitch, yaw, roll). In some embodiments, an IMU rapidly samples the measurement signals and calculates the estimated position of the headset  1605  from the sampled data. For example, the IMU integrates the measurement signals received from the accelerometers over time to estimate a velocity vector and integrates the velocity vector over time to determine an estimated position of a reference point on the headset  1605 . The reference point is a point that may be used to describe the position of the headset  1605 . While the reference point may generally be defined as a point in space, however, in practice the reference point is defined as a point within the headset  1605 . 
     The DCA  1645  generates depth information for a portion of the local area. The DCA includes one or more imaging devices and a DCA controller. The DCA  1645  may also include an illuminator. Operation and structure of the DCA  1645  is described above with regard to  FIG. 1A . 
     The audio system  1650  provides audio content to a user of the headset  1605 . The audio system  1650  is substantially the same as the audio system  200  described above. The audio system  1650  may comprise one or acoustic sensors, one or more transducers, and an audio controller. The audio system  1650  may provide spatialized audio content to the user. In some embodiments, the audio system  1650  may request acoustic parameters from the mapping server  1625  over the network  1620 . The acoustic parameters describe one or more acoustic properties (e.g., room impulse response, a reverberation time, a reverberation level, etc.) of the local area. The audio system  1650  may provide information describing at least a portion of the local area from e.g., the DCA  1645  and/or location information for the headset  1605  from the position sensor  1640 . The audio system  1650  may generate one or more sound filters using one or more of the acoustic parameters received from the mapping server  1625 , and use the sound filters to provide audio content to the user. 
     Transducers of the audio system  1650  may be implemented as described above in detail in relation to  FIGS. 3A through 5E . Components of the audio system  1650  may enhance sound signals as described above in detail in relation to  FIGS. 6 through 11B . Alternatively or additionally, components of the audio system  1650  may perform digital signal processing of audio signals as described above in detail in relation to  FIGS. 12A through 14B . 
     The I/O interface  1610  is a device that allows a user to send action requests and receive responses from the console  1615 . An action request is a request to perform a particular action. For example, an action request may be an instruction to start or end capture of image or video data, or an instruction to perform a particular action within an application. The I/O interface  1610  may include one or more input devices. Example input devices include: a keyboard, a mouse, a game controller, or any other suitable device for receiving action requests and communicating the action requests to the console  1615 . An action request received by the I/O interface  1610  is communicated to the console  1615 , which performs an action corresponding to the action request. In some embodiments, the I/O interface  1610  includes an IMU that captures calibration data indicating an estimated position of the I/O interface  1610  relative to an initial position of the I/O interface  1610 . In some embodiments, the I/O interface  1610  may provide haptic feedback to the user in accordance with instructions received from the console  1615 . For example, haptic feedback is provided when an action request is received, or the console  1615  communicates instructions to the I/O interface  1610  causing the I/O interface  1610  to generate haptic feedback when the console  1615  performs an action. 
     The console  1615  provides content to the headset  1605  for processing in accordance with information received from one or more of: the DCA  1645 , the headset  1605 , and the I/O interface  1610 . In the example shown in  FIG. 16 , the console  1615  includes an application store  1655 , a tracking module  1660 , and an engine  1665 . Some embodiments of the console  1615  have different modules or components than those described in conjunction with  FIG. 16 . Similarly, the functions further described below may be distributed among components of the console  1615  in a different manner than described in conjunction with  FIG. 16 . In some embodiments, the functionality discussed herein with respect to the console  1615  may be implemented in the headset  1605 , or a remote system. 
     The application store  1655  stores one or more applications for execution by the console  1615 . An application is a group of instructions, that when executed by a processor, generates content for presentation to the user. Content generated by an application may be in response to inputs received from the user via movement of the headset  1605  or the I/O interface  1610 . Examples of applications include: gaming applications, conferencing applications, video playback applications, or other suitable applications. 
     The tracking module  1660  tracks movements of the headset  1605  or of the I/O interface  1610  using information from the DCA  1645 , the one or more position sensors  1640 , or some combination thereof. For example, the tracking module  1660  determines a position of a reference point of the headset  1605  in a mapping of a local area based on information from the headset  1605 . The tracking module  1660  may also determine positions of an object or virtual object. Additionally, in some embodiments, the tracking module  1660  may use portions of data indicating a position of the headset  1605  from the position sensor  1640  as well as representations of the local area from the DCA  1645  to predict a future location of the headset  1605 . The tracking module  1660  provides the estimated or predicted future position of the headset  1605  or the I/O interface  1610  to the engine  1665 . 
     The engine  1665  executes applications and receives position information, acceleration information, velocity information, predicted future positions, or some combination thereof, of the headset  1605  from the tracking module  1660 . Based on the received information, the engine  1665  determines content to provide to the headset  1605  for presentation to the user. For example, if the received information indicates that the user has looked to the left, the engine  1665  generates content for the headset  1605  that mirrors the user&#39;s movement in a virtual local area or in a local area augmenting the local area with additional content. Additionally, the engine  1665  performs an action within an application executing on the console  1615  in response to an action request received from the I/O interface  1610  and provides feedback to the user that the action was performed. The provided feedback may be visual or audible feedback via the headset  1605  or haptic feedback via the I/O interface  1610 . 
     The network  1620  couples the headset  1605  and/or the console  1615  to the mapping server  1625 . The network  1620  may include any combination of local area and/or wide area networks using both wireless and/or wired communication systems. For example, the network  1620  may include the Internet, as well as mobile telephone networks. In one embodiment, the network  1620  uses standard communications technologies and/or protocols. Hence, the network  1620  may include links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX), 2G/3G/4G mobile communications protocols, digital subscriber line (DSL), asynchronous transfer mode (ATM), InfiniBand, PCI Express Advanced Switching, etc. Similarly, the networking protocols used on the network  1620  can include multiprotocol label switching (MPLS), the transmission control protocol/Internet protocol (TCP/IP), the User Datagram Protocol (UDP), the hypertext transport protocol (HTTP), the simple mail transfer protocol (SMTP), the file transfer protocol (FTP), etc. The data exchanged over the network  1620  can be represented using technologies and/or formats including image data in binary form (e.g., Portable Network Graphics (PNG)), hypertext markup language (HTML), extensible markup language (XML), etc. In addition, all or some of links can be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), virtual private networks (VPNs), Internet Protocol security (IPsec), etc. 
     The mapping server  1625  may include a database that stores a virtual model describing a plurality of spaces, wherein one location in the virtual model corresponds to a current configuration of a local area of the headset  1605 . The mapping server  1625  receives, from the headset  1605  via the network  1620 , information describing at least a portion of the local area and/or location information for the local area. The user may adjust privacy settings to allow or prevent the headset  1605  from transmitting information to the mapping server  1625 . The mapping server  1625  determines, based on the received information and/or location information, a location in the virtual model that is associated with the local area of the headset  1605 . The mapping server  1625  determines (e.g., retrieves) one or more acoustic parameters associated with the local area, based in part on the determined location in the virtual model and any acoustic parameters associated with the determined location. The mapping server  1625  may transmit the location of the local area and any values of acoustic parameters associated with the local area to the headset  1605 . 
     One or more components of system  1600  may contain a privacy module that stores one or more privacy settings for user data elements. The user data elements describe the user or the headset  1605 . For example, the user data elements may describe a physical characteristic of the user, an action performed by the user, a location of the user of the headset  1605 , a location of the headset  1605 , HRTFs for the user, etc. Privacy settings (or “access settings”) for a user data element may be stored in any suitable manner, such as, for example, in association with the user data element, in an index on an authorization server, in another suitable manner, or any suitable combination thereof. 
     A privacy setting for a user data element specifies how the user data element (or particular information associated with the user data element) can be accessed, stored, or otherwise used (e.g., viewed, shared, modified, copied, executed, surfaced, or identified). In some embodiments, the privacy settings for a user data element may specify a “blocked list” of entities that may not access certain information associated with the user data element. The privacy settings associated with the user data element may specify any suitable granularity of permitted access or denial of access. For example, some entities may have permission to see that a specific user data element exists, some entities may have permission to view the content of the specific user data element, and some entities may have permission to modify the specific user data element. The privacy settings may allow the user to allow other entities to access or store user data elements for a finite period of time. 
     The privacy settings may allow a user to specify one or more geographic locations from which user data elements can be accessed. Access or denial of access to the user data elements may depend on the geographic location of an entity who is attempting to access the user data elements. For example, the user may allow access to a user data element and specify that the user data element is accessible to an entity only while the user is in a particular location. If the user leaves the particular location, the user data element may no longer be accessible to the entity. As another example, the user may specify that a user data element is accessible only to entities within a threshold distance from the user, such as another user of a headset within the same local area as the user. If the user subsequently changes location, the entity with access to the user data element may lose access, while a new group of entities may gain access as they come within the threshold distance of the user. 
     The system  1600  may include one or more authorization/privacy servers for enforcing privacy settings. A request from an entity for a particular user data element may identify the entity associated with the request and the user data element may be sent only to the entity if the authorization server determines that the entity is authorized to access the user data element based on the privacy settings associated with the user data element. If the requesting entity is not authorized to access the user data element, the authorization server may prevent the requested user data element from being retrieved or may prevent the requested user data element from being sent to the entity. Although this disclosure describes enforcing privacy settings in a particular manner, this disclosure contemplates enforcing privacy settings in any suitable manner. 
     Additional Configuration Information 
     The foregoing description of the embodiments has been presented for illustration; it is not intended to be exhaustive or to limit the patent rights to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible considering the above disclosure. 
     Some portions of this description describe the embodiments in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof. 
     Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all the steps, operations, or processes described. 
     Embodiments may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. 
     Embodiments may also relate to a product that is produced by a computing process described herein. Such a product may comprise information resulting from a computing process, where the information is stored on a non-transitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein. 
     Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the patent rights. It is therefore intended that the scope of the patent rights be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments is intended to be illustrative, but not limiting, of the scope of the patent rights, which is set forth in the following claims.