Patent Publication Number: US-11386911-B1

Title: Dereverberation and noise reduction

Description:
BACKGROUND 
     With the advancement of technology, the use and popularity of electronic devices has increased considerably. Electronic devices are commonly used to capture and process audio data. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       For a more complete understanding of the present disclosure, reference is now made to the following description taken in conjunction with the accompanying drawings. 
         FIG. 1  illustrates a system configured to perform dereverberation within a voice processing pipeline according to embodiments of the present disclosure. 
         FIGS. 2A-2C  illustrate examples of frame indexes, tone indexes, and channel indexes. 
         FIG. 3  illustrates example components for performing dereverberation according to examples of the present disclosure. 
         FIG. 4  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure. 
         FIG. 5  illustrates a chart representing reduction in reverberation according to examples of the present disclosure. 
         FIG. 6  is a flowchart conceptually illustrating an example method for performing dereverberation according to embodiments of the present disclosure. 
         FIG. 7  is a flowchart conceptually illustrating an example method for performing dereverberation within a voice processing pipeline according to embodiments of the present disclosure. 
         FIG. 8  illustrates multiple configurations of the reverberation components within the voice processing pipeline according to embodiments of the present disclosure. 
         FIG. 9  is a flowchart conceptually illustrating an example method for performing dereverberation within a voice processing pipeline according to embodiments of the present disclosure. 
         FIG. 10  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure. 
         FIG. 11  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure. 
         FIG. 12  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure. 
         FIG. 13  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure. 
         FIG. 14  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure. 
         FIG. 15  is a block diagram conceptually illustrating example components of a system according to embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Electronic devices may be used to capture and process audio data. The audio data may be used for voice commands and/or may be output by loudspeakers as part of a communication session. During a communication session, loudspeakers may generate audio using playback audio data while a microphone generates local audio data. An electronic device may perform audio processing, such as acoustic echo cancellation, residual echo suppression, noise reduction, and/or the like, to remove audible noise and an “echo” signal corresponding to the playback audio data from the local audio data, isolating local speech to be used for voice commands and/or the communication session. 
     To improve an audio quality during voice communication, devices, systems and methods are disclosed that perform dereverberation and noise reduction during a communication session. For example, a device may apply a two-channel dereverberation algorithm by performing acoustic echo cancellation (AEC) for two microphone signals, calculating coherence-to-diffuse ratio (CDR) values using the outputs of the two AEC components, and calculating dereverberation (DER) gain values based on the CDR values. While the DER gain values may be calculated at a first stage within a voice processing pipeline, the device may apply the DER gain values at a second stage within the voice processing pipeline. For example, the device may calculate the DER gain values prior to performing residual echo suppression (RES) processing but may apply the DER gain values after performing RES processing, in order to avoid excessive attenuation of the local speech. 
     In addition to removing reverberation, the DER gain values may also remove diffuse noise components, reducing an amount of noise reduction required. Thus, the device may perform noise reduction differently when applying the DER gain values. In some examples, the device may perform less aggressive noise reduction processing (e.g., soften the noise reduction processing) when dereverberation is performed by applying the DER gain values, and/or may calculate a noise estimate after applying the DER gain values. In other examples, the device may only apply the DER gain values when a signal-to-noise ratio (SNR) value is above a threshold value. Thus, when the SNR value is relatively high, the device may perform dereverberation by applying the DER gain values. In contrast, when the SNR value is relatively low, indicating that noisy conditions are present, the device may skip dereverberation and prioritize noise reduction processing. 
       FIG. 1  illustrates a high-level conceptual block diagram of a system  100  configured to perform dereverberation within a voice processing pipeline. As illustrated in  FIG. 1 , the system  100  may include a device  110  that may be communicatively coupled to network(s)  199  and may include one or more microphone(s)  112  in a microphone array and/or one or more loudspeaker(s)  114 . However, the disclosure is not limited thereto and the device  110  may include additional components without departing from the disclosure. 
     The device  110  may be an electronic device configured to send audio data to and/or receive audio data. For example, the device  110  (e.g., local device) may receive playback audio data (e.g., far-end reference audio data, represented in  FIG. 1  as far-end reference signal(s) X(n, k)) from a remote device and the playback audio data may include remote speech originating at the remote device. During a communication session, the device  110  may generate output audio corresponding to the playback audio data using the one or more loudspeaker(s)  114 . While generating the output audio, the device  110  may capture microphone audio data (e.g., input audio data, represented in  FIG. 1  as microphone signals Z(n, k)) using the one or more microphone(s)  112 . In addition to capturing desired speech (e.g., the microphone audio data includes a representation of local speech from a user  10 , represented in  FIG. 1  as near-end speech s(t)), the device  110  may capture a portion of the output audio generated by the loudspeaker(s)  114  (including a portion of the remote speech), which may be referred to as an “echo” or echo signal y(t), along with additional acoustic noise n(t) (e.g., undesired speech, ambient acoustic noise in an environment around the device  110 , etc.), as discussed in greater detail below. 
     For ease of illustration, some audio data may be referred to as a signal, such as a far-end reference signal(s) x(t), an echo signal y(t), an echo estimate signal y′(t), microphone signals z(t), isolated signal(s) m(t) (e.g., error signal m(t)), and/or the like. However, the signals may be comprised of audio data and may be referred to as audio data (e.g., far-end reference audio data x(t), echo audio data y(t), echo estimate audio data y′(t), microphone audio data z(t), isolated audio data m(t), error audio data m(t), etc.) without departing from the disclosure. 
     As will be described in greater detail below with regard to  FIGS. 2A-2C , an audio signal may be represented in the time domain (e.g., far-end reference signal(s) x(t)) or in a frequency/subband domain (e.g., far-end reference signal(s) X(n, k)) without departing from the disclosure. In some examples, audio signals generated by microphones  112 , output to the loudspeaker(S)  114 , and/or sent via network(s)  199  are time domain signals (e.g., x(t)), and the device  110  converts these time domain signals to the frequency/subband domain during audio processing. For ease of illustration, however,  FIG. 1  represents the far-end reference signal(s) X(n, k), the microphone signals Z(n, k), and the output signal OUT(n, k) in the frequency/subband domain. 
     During a communication session, the device  110  may receive far-end reference signal(s) x(t) (e.g., playback audio data) from a remote device/remote server(s) via the network(s)  199  and may generate output audio (e.g., playback audio) based on the far-end reference signal(s) x(t) using the one or more loudspeaker(s)  114 . Using one or more microphone(s)  112  in the microphone array, the device  110  may capture input audio as microphone signals z(t) (e.g., near-end reference audio data, input audio data, microphone audio data, etc.), may perform audio processing to the microphone signals z(t) to generate an output signal out(t) (e.g., output audio data), and may send the output signal out(t) to the remote device/remote server(s) via the network(s)  199 . 
     In some examples, the device  110  may send the output signal out(t) to the remote device as part of a Voice over Internet Protocol (VoIP) communication session. For example, the device  110  may send the output signal out(t) to the remote device either directly or via remote server(s) and may receive the far-end reference signal(s) x(t) from the remote device either directly or via the remote server(s). However, the disclosure is not limited thereto and in some examples, the device  110  may send the output signal out(t) to the remote server(s) in order for the remote server(s) to determine a voice command. For example, during a communication session the device  110  may receive the far-end reference signal(s) x(t) from the remote device and may generate the output audio based on the far-end reference signal(s) x(t). However, the microphone signal z(t) may be separate from the communication session and may include a voice command directed to the remote server(s). Therefore, the device  110  may send the output signal out(t) to the remote server(s) and the remote server(s) may determine a voice command represented in the output signal out(t) and may perform an action corresponding to the voice command (e.g., execute a command, send an instruction to the device  110  and/or other devices to execute the command, etc.). In some examples, to determine the voice command the remote server(s) may perform Automatic Speech Recognition (ASR) processing, Natural Language Understanding (NLU) processing and/or command processing. The voice commands may control the device  110 , audio devices (e.g., play music over loudspeaker(s)  114 , capture audio using microphone(s)  112 , or the like), multimedia devices (e.g., play videos using a display, such as a television, computer, tablet or the like), smart home devices (e.g., change temperature controls, turn on/off lights, lock/unlock doors, etc.) or the like. 
     In audio systems, acoustic echo cancellation (AEC) processing refers to techniques that are used to recognize when a device has recaptured sound via microphone(s) after some delay that the device previously output via loudspeaker(s). The device may perform AEC processing by subtracting a delayed version of the original audio signal (e.g., far-end reference signal(s) X(n, k)) from the captured audio (e.g., microphone signal(s) Z(n, k)), producing a version of the captured audio that ideally eliminates the “echo” of the original audio signal, leaving only new audio information. For example, if someone were singing karaoke into a microphone while prerecorded music is output by a loudspeaker, AEC processing can be used to remove any of the recorded music from the audio captured by the microphone, allowing the singer&#39;s voice to be amplified and output without also reproducing a delayed “echo” of the original music. As another example, a media player that accepts voice commands via a microphone can use AEC processing to remove reproduced sounds corresponding to output media that are captured by the microphone, making it easier to process input voice commands. 
     The device  110  may perform audio processing to the microphone signals Z(n, k) to generate the output signal OUT(n, k). For example, the device  110  may input the microphone signal(s) Z(n, k) to a voice processing pipeline and may perform a series of steps to improve an audio quality associated with the output signal OUT(n, k). As illustrated in  FIG. 1 , the device  110  may perform acoustic echo cancellation (AEC) processing, residual echo suppression (RES) processing, noise reduction (NR) processing, dereverberation (DER) processing, and/or other audio processing to isolate local speech captured by the microphone(s)  112  and/or to suppress unwanted audio data (e.g., echoes and/or noise). For example, the device  110  may include an AEC component  120  configured to perform AEC processing to perform echo cancellation, a RES component  122  configured to perform RES processing to suppress a residual echo signal, a noise component  124  configured to perform NR processing to attenuate a noise signal, and a DER component  126  configured to perform DER processing to reduce and/or remove reverberation. 
     As illustrated in  FIG. 1 , the device  110  may receive the far-end reference signal(s) (e.g., playback audio data) and may generate playback audio (e.g., echo signal y(t)) using the loudspeaker(s)  114 . While the device  110  may generate the playback audio using the far-end reference signal(s) x(t) in the time domain, for ease of illustration  FIG. 1  represents the far-end reference signal(s) X(n, k) in the frequency/subband domain as the AEC component  120  performs echo cancellation in the subband domain. The far-end reference signal(s) may be referred to as far-end reference signal(s) (e.g., far-end reference audio data), playback signal(s) (e.g., playback audio data), and/or the like. 
     The one or more microphone(s)  112  in the microphone array may capture microphone signals (e.g., microphone audio data, near-end reference signals, input audio data, etc.), which may include the echo signal y(t) along with near-end speech s(t) from the user  10  and noise n(t). While the device  110  may generate the microphone signals z(t) in the time domain, for ease of illustration  FIG. 1  represents the microphone signals Z(n, k) in the frequency/subband domain as the AEC component  120  performs echo cancellation in the subband domain. 
     To isolate the local speech (e.g., near-end speech s(t) from the user  10 ), the device  110  may include the AEC component  120 , which may subtract a portion of the far-end reference signal(s) X(n, k) from the microphone signal(s) Z(n, k) and generate isolated signal(s) M(n, k) (e.g., error signal(s)). As the AEC component  120  does not have access to the echo signal y(t) itself, the AEC component  120  and/or an additional component (not illustrated) may use the far-end reference signal(s) X(n, k) to generate reference signal(s) (e.g., estimated echo signal(s)), which corresponds to the echo signal y(t). Thus, when the AEC component  120  removes the reference signal(s), the AEC component  120  is removing at least a portion of the echo signal y(t). Therefore, the output (e.g., isolated signal(s) M(n, k)) of the AEC component  120  may include the near-end speech s(t) along with portions of the echo signal y(t) and/or the noise n(t) (e.g., difference between the reference signal(s) and the actual echo signal y(t) and noise n(t)). 
     To improve the audio data, in some examples the RES component  122  may perform RES processing to the isolated signal(s) M(n, k) in order to dynamically suppress unwanted audio data (e.g., the portions of the echo signal y(t) and the noise n(t) that were not removed by the AEC component  120 ). For example, the RES component  122  may attenuate the isolated signal(s) M(n, k) to generate a first audio signal R(n, k). Performing the RES processing may remove and/or reduce the unwanted audio data from the first audio signal R(n, k). However, the device  110  may disable RES processing in certain conditions, such as when near-end speech s(t) is present in the isolated signal(s) M(n, k) (e.g., near-end single talk conditions or double-talk conditions are present). For example, when the device  110  detects that the near-end speech s(t) is present in the isolated signal(s) M(n, k), the RES component  122  may act as a pass-through filter and pass the isolated signal(s) M(n, k) with minor attenuation and/or without any attenuation, although the disclosure is not limited thereto. This avoids attenuating the near-end speech s(t). While not illustrated in  FIG. 1 , in some examples the device  110  may include a double-talk detector configured to determine when near-end speech and/or far-end speech is present in the isolated signal(s) M(n, k). 
     Residual echo suppression (RES) processing is performed by selectively attenuating, based on individual frequency bands, an isolated audio signal M(n, k) output by the AEC component  120  to generate the first audio signal R(n, k) output by the RES component  122 . For example, performing RES processing may determine a gain for a portion of the isolated audio signal M(n, k) corresponding to a specific frequency band (e.g., 100 Hz to 200 Hz) and may attenuate the portion of the isolated audio signal M(n, k) based on the gain to generate a portion of the first audio signal R(n, k) corresponding to the specific frequency band. Thus, a gain may be determined for each frequency band and therefore the amount of attenuation may vary based on the frequency band. 
     The device  110  may determine the gain based on an attenuation value. For example, a low attenuation value cu (e.g., closer to a value of zero) results in a gain that is closer to a value of one and therefore an amount of attenuation is relatively low. In some examples, the RES component  122  may operate similar to a pass-through filter for low frequency bands, although the disclosure is not limited thereto. An energy level of the first audio signal R(n, k) is therefore similar to an energy level of the isolated audio signal M(n, k). In contrast, a high attenuation value as (e.g., closer to a value of one) results in a gain that is closer to a value of zero and therefore an amount of attenuation is relatively high. In some examples, the RES component  122  may attenuate high frequency bands, such that an energy level of the first audio signal R(n, k) is lower than an energy level of the isolated audio signal M(n, k), although the disclosure is not limited thereto. In these examples, the energy level of the first audio signal R(n, k) corresponding to the high frequency bands is lower than the energy level of the first audio signal R(n, k) corresponding to the low frequency bands. 
     Room reverberation is a detrimental factor that negatively impacts audio quality for hands-free devices, such as the device  110 . For example, a user  10  of the device  110  may establish a communication session with another device, where digitized speech signals are compressed, packetized, and transmitted via the network(s)  199 . One technique for establishing the communication session involves Voice over Internet Protocol (VoIP), although the disclosure is not limited thereto. During the communication session, a large amount of reverberation is harmful to communication (e.g., reduces an audio quality), as the reverberation lowers intelligibility and makes the speech sound “far” and “hollow.” The reverberation is caused by walls and other hard surfaces in an environment of the device  110  (e.g., inside a room) creating multiple reflections. These reflections can be classified as early and late depending on a time-of-arrival associated with an individual reflection. Early reflections typically do not impact the audio quality, but late reflections may decrease the audio quality. 
     A dereverberation algorithm suppresses the late reverberation in the speech signal, providing an enhanced listening experience to the users during the communication session. However, applying a real-time dereverberation algorithm and integrating it into a voice processing pipeline may affect a performance of other components within the voice processing pipeline. For example, complications arise when the dereverberator affects the performance of components such as the noise component  124  configured to perform noise reduction processing. 
     To reduce the impact of applying dereverberation processing, the device  110  may modify the operation of other components in the voice processing pipeline and/or may tune dereverberator parameters associated with the dereverberation processing. However, tuning the dereverberator parameters may pose additional challenges, as accurate models to quantify a subjective perception of reverberant components in speech signals do not exist. In contrast, objective speech quality assessment methods (e.g., Perceptual Objective Listening Quality Analysis (POLQA)) do not take into account reverberation. Thus, they are not accurate and cannot be used to evaluate reverberant signals. 
     As described in greater detail below with regard to  FIG. 3 , the DER component  126  may calculate dereverberation (DER) gain values by determining coherence-to-diffuse ratio (CDR) values between a first isolated signal M 1 (n, k) associated with a first microphone  112   a  and a second isolated signal M 2 (n, k) associated with a second microphone  112   b . The DER component  126  may use the CDR values (e.g., CDR data) to generate a plurality of DER gain values (e.g., DER gain data) and may send the plurality of DER gain values to the RES component  122 . In addition to performing RES processing as described above, in some examples the RES component  122  may apply the DER gain values to perform dereverberation processing. 
     While the description above refers to the RES component  122  performing RES processing to generate the first audio signal R(n, k), this only applies when the device  110  determines not to perform dereverberation processing. If the device  110  determines to perform dereverberation process, the RES component  122  may perform RES processing to the isolated signal M(n, k) to generate a first audio signal and then may apply the DER gain values to the first audio signal to generate a second audio signal R(n, k). However, while  FIG. 1  illustrates an example of the RES component  122  receiving the DER gain values from the DER component  126 , the disclosure is not limited thereto and the device  110  may apply the DER gain values using other components without departing from the disclosure, as described in greater detail below. 
     After the RES component  122  generates the first audio signal R(n, k), the noise component  124  may perform noise reduction processing on the first audio signal R(n, k) to generate an output signal out(t). For example, the noise component  124  may apply aggressive noise reduction when conditions are noisy (e.g., SNR value is low or below a threshold value), but may apply less aggressive noise reduction when conditions are quiet and/or when the DER gain values are applied to perform dereverberation. The noise component  124  will be described in greater detail below with regard to  FIG. 4 . 
     Although  FIG. 1 , and other figures/discussion illustrate the operation of the system in a particular order, the steps described may be performed in a different order (as well as certain steps removed or added) without departing from the intent of the disclosure. For example, the DER component  126  may be placed prior to the AEC component  120  without departing from the disclosure. Additionally or alternatively, the device  110  may apply the DER gain values before the AEC component  120 , after the AEC component  120 , after the RES component  122 , during noise reduction processing, and/or the like without departing from the disclosure. 
     As illustrated in  FIG. 1 , the device  110  may perform ( 140 ) echo cancellation to generate isolated signals. For example, the AEC component  120  may perform first AEC processing to generate a first isolated signal M 1 (n, k) associated with a first microphone  112   a  and may perform second AEC processing to generate a second isolated signal M 2 (n, k) associated with a second microphone  112   b . The AEC component  120  may perform the first AEC processing by subtracting a portion of the far-end reference signal(s) X(n, k) from a first microphone signal Z 1 (n, k) to generate the first isolated signal M 1 (n, k). Similarly, the AEC component  120  may perform the second AEC processing by subtracting a portion of the far-end reference signal(s) X(n, k) from a second microphone signal Z 2 (n, k) to generate the second isolated signal M 2 (n, k). 
     Using the first isolated signal M 1 (n, k) and the second isolated signal M 2 (n, k), the device  110  may determine ( 142 ) a noise estimate corresponding to noise components of the first isolated signal M 1 (k, n), determine ( 144 ) coherence-to-diffuse ratio (CDR) values, and determine ( 146 ) DER gain values using the CDR values. These steps will be described in greater detail below with regard to  FIG. 3 . In some examples, the device  110  may use the noise estimate to determine whether to perform dereverberation processing, although the disclosure is not limited thereto. 
     As described above, the device  110  may perform ( 148 ) residual echo suppression (RES) processing, may perform ( 150 ) dereverberation (DER) processing using the DER gain values, and may perform ( 152 ) noise reduction processing using the noise estimate. In some examples, the device  110  may determine a new noise estimate after applying the DER gain values, as described in greater detail below with regard to  FIG. 4 . While  FIG. 1  illustrates an example in which the DER processing is performed after RES processing and before NR processing, the disclosure is not limited thereto and the order of these steps may vary without departing from the disclosure. For example, the DER processing may be performed prior to the RES processing, after the RES processing, or as part of NR processing without departing from the disclosure. 
     In some examples, the device  110  may operate using a microphone array comprising multiple microphones  112 . For example, the device  110  may use three or more microphones  112  to determine the CDR values and/or the DER gain values without departing from the disclosure. In some examples, the device  110  may select microphone pairs from a plurality of microphones  112  without departing from the disclosure. Additionally or alternatively, the device  110  may apply beamforming to generate a plurality of directional audio signals (e.g., beams) and may determine the CDR values and/or the DER gain values using two or more beams instead of microphone audio signals without departing from the disclosure. In audio systems, beamforming refers to techniques that are used to isolate audio from a particular direction in a multi-directional audio capture system. Beamforming may be particularly useful when filtering out noise from non-desired directions. Beamforming may be used for various tasks, including isolating voice commands to be executed by a speech-processing system. 
     One technique for beamforming involves boosting audio received from a desired direction while dampening audio received from a non-desired direction. In one example of a beamformer system, a fixed beamformer unit employs a filter-and-sum structure to boost an audio signal that originates from the desired direction (sometimes referred to as the look-direction) while largely attenuating audio signals that original from other directions. A fixed beamformer unit may effectively eliminate certain diffuse noise (e.g., undesirable audio), which is detectable in similar energies from various directions, but may be less effective in eliminating noise emanating from a single source in a particular non-desired direction. The beamformer unit may also incorporate an adaptive beamformer unit/noise canceller that can adaptively cancel noise from different directions depending on audio conditions. 
     As an alternative to performing acoustic echo cancellation using the far-end reference signal(s) X(n, k), in some examples the device  110  may generate a reference signal based on the beamforming. For example, the device  110  may use Adaptive Reference Algorithm (ARA) processing to generate an adaptive reference signal based on the microphone signal(s) Z(n, k). To illustrate an example, the ARA processing may perform beamforming using the microphone signal(s) Z(n, k) to generate a plurality of audio signals (e.g., beamformed audio data) corresponding to particular directions. For example, the plurality of audio signals may include a first audio signal corresponding to a first direction, a second audio signal corresponding to a second direction, a third audio signal corresponding to a third direction, and so on. The ARA processing may select the first audio signal as a target signal (e.g., the first audio signal includes a representation of speech) and the second audio signal as a reference signal (e.g., the second audio signal includes a representation of the echo and/or other acoustic noise) and may perform Adaptive Interference Cancellation (AIC) (e.g., adaptive acoustic interference cancellation) by removing the reference signal from the target signal. As the input audio data is not limited to the echo signal, the ARA processing may remove other acoustic noise represented in the input audio data in addition to removing the echo. Therefore, the ARA processing may be referred to as performing AIC, adaptive noise cancellation (ANC), AEC, and/or the like without departing from the disclosure. 
     In some examples, the device  110  may be configured to perform AIC using the ARA processing to isolate the speech in the microphone signal(s) Z(n, k). The device  110  may dynamically select target signal(s) and/or reference signal(s). Thus, the target signal(s) and/or the reference signal(s) may be continually changing over time based on speech, acoustic noise(s), ambient noise(s), and/or the like in an environment around the device  110 . In some examples, the device  110  may select the target signal(s) based on signal quality metrics (e.g., signal-to-interference ratio (SIR) values, signal-to-noise ratio (SNR) values, average power values, etc.) differently based on current system conditions. For example, the device  110  may select target signal(s) having highest signal quality metrics during near-end single-talk conditions (e.g., to increase an amount of energy included in the target signal(s)), but select the target signal(s) having lowest signal quality metrics during far-end single-talk conditions (e.g., to decrease an amount of energy included in the target signal(s)). 
     In some examples, the device  110  may perform AIC processing without performing beamforming without departing from the disclosure. Instead, the device  110  may select target signals and/or reference signals from the microphone signal(s) Z(n, k) without performing beamforming. For example, a first microphone  112   a  may be positioned in proximity to the loudspeaker(s)  114  or other sources of acoustic noise while a second microphone  112   b  may be positioned in proximity to the user  10 . Thus, the device  110  may select first microphone signal Z 1 (n, k) associated with the first microphone  112   a  as the reference signal and may select second microphone signal Z 2 (n, k) associated with the second microphone  112   b  as the target signal without departing from the disclosure. Additionally or alternatively, the device  110  may select the target signals and/or the reference signals from a combination of the beamformed audio data and the microphone signal(s) Z(n, k) without departing from the disclosure. 
     While  FIG. 1  illustrates the loudspeaker(s)  114  being internal to the device  110 , the disclosure is not limited thereto and the loudspeaker(s)  114  may be external to the device  110  without departing from the disclosure. For example, the device  110  may send the far-end reference signal(s) x(t) to the loudspeaker(s)  114  using a wireless protocol without departing from the disclosure. However, the disclosure is not limited thereto and the loudspeaker(s)  114  may be included in the device  110  and/or connected via a wired connection without departing from the disclosure. For example, the loudspeaker(s)  114  may correspond to a wireless loudspeaker, a television, an audio system, and/or the like connected to the device  110  using a wireless and/or wired connection without departing from the disclosure. 
     An audio signal is a representation of sound and an electronic representation of an audio signal may be referred to as audio data, which may be analog and/or digital without departing from the disclosure. For ease of illustration, the disclosure may refer to either audio data (e.g., far-end reference audio data or playback audio data, microphone audio data, near-end reference data or input audio data, etc.) or audio signals (e.g., playback signal, far-end reference signal, microphone signal, near-end reference signal, etc.) without departing from the disclosure. Additionally or alternatively, portions of a signal may be referenced as a portion of the signal or as a separate signal and/or portions of audio data may be referenced as a portion of the audio data or as separate audio data. For example, a first audio signal may correspond to a first period of time (e.g., 30 seconds) and a portion of the first audio signal corresponding to a second period of time (e.g., 1 second) may be referred to as a first portion of the first audio signal or as a second audio signal without departing from the disclosure. Similarly, first audio data may correspond to the first period of time (e.g., 30 seconds) and a portion of the first audio data corresponding to the second period of time (e.g., 1 second) may be referred to as a first portion of the first audio data or second audio data without departing from the disclosure. Audio signals and audio data may be used interchangeably, as well; a first audio signal may correspond to the first period of time (e.g., 30 seconds) and a portion of the first audio signal corresponding to a second period of time (e.g., 1 second) may be referred to as first audio data without departing from the disclosure. 
     As used herein, audio signals or audio data (e.g., far-end reference audio data, near-end reference audio data, microphone audio data, or the like) may correspond to a specific range of frequency bands. For example, far-end reference audio data and/or near-end reference audio data may correspond to a human hearing range (e.g., 20 Hz-20 kHz), although the disclosure is not limited thereto. 
     Far-end reference audio data (e.g., far-end reference signal(s) x(t)) corresponds to audio data that will be output by the loudspeaker(s)  114  to generate playback audio (e.g., echo signal y(t)). For example, the device  110  may stream music or output speech associated with a communication session (e.g., audio or video telecommunication). In some examples, the far-end reference audio data may be referred to as playback audio data, loudspeaker audio data, and/or the like without departing from the disclosure. For ease of illustration, the following description will refer to the playback audio data as far-end reference audio data. As noted above, the far-end reference audio data may be referred to as far-end reference signal(s) x(t) without departing from the disclosure. As described above, the far-end reference signal(s) may be represented in a time domain (e.g., x(t)) or a frequency/subband domain (e.g., X(n, k)) without departing from the disclosure. 
     Microphone audio data corresponds to audio data that is captured by the microphone(s)  112  prior to the device  110  performing audio processing such as AIC processing. The microphone audio data may include local speech s(t) (e.g., an utterance, such as near-end speech generated by the user  10 ), an “echo” signal y(t) (e.g., portion of the playback audio captured by the microphone(s)  112 ), acoustic noise n(t) (e.g., ambient noise in an environment around the device  110 ), and/or the like. As the microphone audio data is captured by the microphone(s)  112  and captures audio input to the device  110 , the microphone audio data may be referred to as input audio data, near-end audio data, and/or the like without departing from the disclosure. For ease of illustration, the following description will refer to microphone audio data and near-end reference audio data interchangeably. As noted above, the near-end reference audio data/microphone audio data may be referred to as a near-end reference signal(s) or microphone signal(s) without departing from the disclosure. As described above, the microphone signals may be represented in a time domain (e.g., z(t)) or a frequency/subband domain (e.g., Z(n, k)) without departing from the disclosure. 
     An “echo” signal y(t) corresponds to a portion of the playback audio that reaches the microphone(s)  112  (e.g., portion of audible sound(s) output by the loudspeaker(s)  114  that is recaptured by the microphone(s)  112 ) and may be referred to as an echo or echo data y(t). 
     Output audio data corresponds to audio data after the device  110  performs audio processing (e.g., AIC processing, ANC processing, AEC processing, and/or the like) to isolate the local speech s(t). For example, the output audio data corresponds to the microphone audio data Z(n, k) after subtracting the reference signal(s) X(n, k) (e.g., using adaptive interference cancellation (AIC) component  120 ), optionally performing residual echo suppression (RES) (e.g., using the RES component  122 ), and/or other audio processing known to one of skill in the art. As noted above, the output audio data may be referred to as output audio signal(s) without departing from the disclosure. As described above, the output signal may be represented in a time domain (e.g., out(t)) or a frequency/subband domain (e.g., OUT(n, k)) without departing from the disclosure. 
     As illustrated in  FIG. 1 , the output of the AEC component may be represented as M(n, k) and may be referred to as isolated audio signal M(n, k), error audio data M(n, k), error signal M(n, k), and/or the like. Similarly, the output of the RES component  122  may be represented as R(n, k) and may be referred to as a first audio signal R(n, k), while the output of the noise component  124  may be represented as OUT(n, k) and may be referred to as an output signal OUT(n, k). 
     For ease of illustration, the following description may refer to generating the output audio data by performing acoustic echo cancellation (AEC) processing, residual echo suppression (RES) processing, noise reduction (NR) processing, and/or dereverberation (DER) processing. However, the disclosure is not limited thereto, and the device  110  may generate the output audio data by performing AEC processing, AIC processing, RES processing, NR processing, DER processing, other audio processing, and/or a combination thereof without departing from the disclosure. Additionally or alternatively, the disclosure is not limited to AEC processing and, in addition to or instead of performing AEC processing, the device  110  may perform other processing to remove or reduce unwanted speech s 2 (t) (e.g., speech associated with a second user), unwanted acoustic noise n(t), and/or echo signals y(t), such as adaptive interference cancellation (AIC) processing, adaptive noise cancellation (ANC) processing, and/or the like without departing from the disclosure. 
       FIGS. 2A-2C  illustrate examples of frame indexes, tone indexes, and channel indexes. As described above, the device  110  may generate microphone audio data z(t) using microphones  112 . For example, a first microphone  112   a  may generate first microphone audio data z 1 (t) in a time domain, a second microphone  112   b  may generate second microphone audio data z 2 (t) in the time domain, and so on. As illustrated in  FIG. 2A , a time domain signal may be represented as microphone audio data z(t)  210 , which is comprised of a sequence of individual samples of audio data. Thus, z(t) denotes an individual sample that is associated with a time t. 
     While the microphone audio data z(t)  210  is comprised of a plurality of samples, in some examples the device  110  may group a plurality of samples and process them together. As illustrated in  FIG. 2A , the device  110  may group a number of samples together in a frame (e.g., audio frame) to generate microphone audio data z(n)  212 . As used herein, a variable z(n) corresponds to the time-domain signal and identifies an individual frame (e.g., fixed number of samples s) associated with a frame index n. 
     Additionally or alternatively, the device  110  may convert microphone audio data z(n)  212  from the time domain to the frequency domain or subband domain. For example, the device  110  may perform Discrete Fourier Transforms (DFTs) (e.g., Fast Fourier transforms (FFTs), short-time Fourier Transforms (STFTs), and/or the like) to generate microphone audio data Z(n, k)  214  in the frequency domain or the subband domain. As used herein, a variable Z(n, k) corresponds to the frequency-domain signal and identifies an individual frame associated with frame index n and tone index k. As illustrated in  FIG. 2A , the microphone audio data z(t)  210  corresponds to time indexes  216 , whereas the microphone audio data z(n)  212  and the microphone audio data Z(n, k)  214  corresponds to frame indexes  218 . 
     While  FIG. 2A  illustrates examples of the device  110  converting between microphone audio data z(t)  210  (e.g., time domain signal comprising individual samples), microphone audio data z(n)  212  (e.g., time domain signal comprising audio frames), and microphone audio data Z(n, k)  214  (e.g., frequency domain or subband domain signal), the disclosure is not limited thereto and these concepts may be applied to other audio signals without departing from the disclosure. For example, the device  110  may convert between reference audio data x(t) (e.g., time domain signal comprising individual samples), reference audio data x(n) (e.g., time domain signal comprising audio frames), and reference audio data X(n, k) (e.g., frequency domain or subband domain signal) without departing from the disclosure. Similarly, the device  110  may generate an output signal OUT(n, k) in the frequency or subband domain and then convert to the time domain to generate output signal out(n) or out(t) without departing from the disclosure. 
     A Fast Fourier Transform (FFT) is a Fourier-related transform used to determine the sinusoidal frequency and phase content of a signal, and performing FFT produces a one-dimensional vector of complex numbers. This vector can be used to calculate a two-dimensional matrix of frequency magnitude versus frequency. In some examples, the system  100  may perform FFT on individual frames of audio data and generate a one-dimensional and/or a two-dimensional matrix corresponding to the microphone audio data Z(n). However, the disclosure is not limited thereto and the system  100  may instead perform short-time Fourier transform (STFT) operations without departing from the disclosure. A short-time Fourier transform is a Fourier-related transform used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. 
     Using a Fourier transform, a sound wave such as music or human speech can be broken down into its component “tones” of different frequencies, each tone represented by a sine wave of a different amplitude and phase. Whereas a time-domain sound wave (e.g., a sinusoid) would ordinarily be represented by the amplitude of the wave over time, a frequency domain representation of that same waveform comprises a plurality of discrete amplitude values, where each amplitude value is for a different tone or “bin.” So, for example, if the sound wave consisted solely of a pure sinusoidal 1 kHz tone, then the frequency domain representation would consist of a discrete amplitude spike in the bin containing 1 kHz, with the other bins at zero. In other words, each tone “k” is a frequency index (e.g., frequency bin). 
       FIG. 2A  illustrates an example of time indexes  216  (e.g., microphone audio data z(t)  210 ) and frame indexes  218  (e.g., microphone audio data z(n)  212  in the time domain and microphone audio data Z(n, k)  216  in the frequency domain or subband domain). For example, the system  100  may apply FFT processing to the time-domain microphone audio data z(n)  212 , producing the frequency-domain microphone audio data Z(n, k)  214 , where the tone index “k” (e.g., frequency index) ranges from 0 to K and “n” is a frame index ranging from 0 to N. As illustrated in  FIG. 2A , the history of the values across iterations is provided by the frame index “n”, which ranges from 1 to N and represents a series of samples over time. 
       FIG. 2B  illustrates an example of performing a K-point FFT on a time-domain signal. As illustrated in  FIG. 2B , if a 256-point FFT is performed on a 16 kHz time-domain signal, the output is 256 complex numbers, where each complex number corresponds to a value at a frequency in increments of 16 kHz/256, such that there is 62.5 Hz between points, with point 0 corresponding to 0 Hz and point 255 corresponding to 16 kHz. As illustrated in  FIG. 2B , each tone index  220  in the 256-point FFT corresponds to a frequency range (e.g., subband) in the 16 kHz time-domain signal. While  FIG. 2B  illustrates the frequency range being divided into 256 different subbands (e.g., tone indexes), the disclosure is not limited thereto and the system  100  may divide the frequency range into K different subbands or frequency bins (e.g., K indicates an FFT size) without departing from the disclosure. While  FIG. 2B  illustrates the tone index  220  being generated using a Fast Fourier Transform (FFT), the disclosure is not limited thereto. Instead, the tone index  220  may be generated using Short-Time Fourier Transform (STFT), generalized Discrete Fourier Transform (DFT) and/or other transforms known to one of skill in the art (e.g., discrete cosine transform, non-uniform filter bank, etc.). 
     The system  100  may include multiple microphones  112 , with a first channel (m=1) corresponding to a first microphone  112   a , a second channel (m=2) corresponding to a second microphone  112   b , and so on until an M-th channel (m=M) that corresponds to microphone  112 M.  FIG. 2C  illustrates channel indexes  230  including a plurality of channels from channel ml to channel M. While many drawings illustrate two channels (e.g., two microphones  112 ), the disclosure is not limited thereto and the number of channels may vary. For the purposes of discussion, an example of system  100  includes “M” microphones  112  (M&gt;1) for hands free near-end/far-end distant speech recognition applications. 
     Similarly, the system  100  may include multiple loudspeakers  114 , with a first channel (x=1) corresponding to a first loudspeaker  114   a , a second channel (x=2) corresponding to a second loudspeaker  114   b , and so on until an X-th channel (x=X) that corresponds to loudspeaker  114 X.  FIG. 2C  illustrates channel indexes  230  also including a plurality of reference channels from channel x1 to channel X. For ease of illustration, the following disclosure may refer to a single reference channel, but the disclosure is not limited thereto and the system  100  may modify the techniques described herein based on any number of reference channels without departing from the disclosure. 
     As described above, while  FIG. 2A  is described with reference to the microphone audio data z(t), the disclosure is not limited thereto and the same techniques apply to the playback audio data x(t) without departing from the disclosure. Thus, playback audio data x(t) indicates a specific time index t from a series of samples in the time-domain, playback audio data x(n) indicates a specific frame index n from series of frames in the time-domain, and playback audio data X(n, k) indicates a specific frame index n and frequency index k from a series of frames in the frequency-domain. 
     Prior to converting the microphone audio data z(n) and the playback audio data x(n) to the frequency-domain, the device  110  may first perform time-alignment to align the playback audio data x(n) with the microphone audio data z(n). For example, due to nonlinearities and variable delays associated with sending the playback audio data x(n) to the loudspeaker(s)  114  (e.g., especially if using a wireless connection), the playback audio data x(n) is not synchronized with the microphone audio data z(n). This lack of synchronization may be due to a propagation delay (e.g., fixed time delay) between the playback audio data x(n) and the microphone audio data z(n), clock jitter and/or clock skew (e.g., difference in sampling frequencies between the device  110  and the loudspeaker(s)  114 ), dropped packets (e.g., missing samples), and/or other variable delays. 
     To perform the time alignment, the device  110  may adjust the playback audio data x(n) to match the microphone audio data z(n). For example, the device  110  may adjust an offset between the playback audio data x(n) and the microphone audio data z(n) (e.g., adjust for propagation delay), may add/subtract samples and/or frames from the playback audio data x(n) (e.g., adjust for drift), and/or the like. In some examples, the device  110  may modify both the microphone audio data and the playback audio data in order to synchronize the microphone audio data and the playback audio data. However, performing nonlinear modifications to the microphone audio data results in first microphone audio data associated with a first microphone to no longer be synchronized with second microphone audio data associated with a second microphone. Thus, the device  110  may instead modify only the playback audio data so that the playback audio data is synchronized with the first microphone audio data. 
     As described above, room reverberation is a detrimental factor that negatively impacts audio quality for hands-free voice communication systems. For example, a user  10  of a local device  110  may establish a communication session with another device, where digitized speech signals are compressed, packetized, and transmitted via the network(s)  199 . One technique for establishing the communication session involves Voice over Internet Protocol (VoIP), although the disclosure is not limited thereto. During the communication session, a large amount of reverberation is harmful to communication (e.g., reduces an audio quality), as the reverberation lowers intelligibility and makes the speech sound “far” and “hollow.” The reverberation is caused by walls and other hard surfaces in an environment of the device  110  (e.g., inside a room) creating multiple reflections. These reflections can be classified as early and late depending on a time-of-arrival associated with an individual reflection. Early reflections typically do not impact the audio quality, but late reflections may decrease the audio quality. 
     A dereverberation algorithm suppresses the late reverberation in the speech signal, providing an enhanced listening experience to the users during the communication session. However, applying a real-time dereverberation algorithm and integrating it into a voice processing pipeline may affect a performance of other components within the voice processing pipeline. For example, complications arise when the dereverberator affects the performance of components such as the noise component  124  configured to perform noise reduction processing. 
     To reduce the impact of applying dereverberation processing, the device  110  may modify the operation of other components in the voice processing pipeline and/or may tune dereverberator parameters associated with the dereverberation processing. However, tuning the dereverberator parameters may pose additional challenges, as accurate models to quantify a subjective perception of reverberant components in speech signals do not exist. In contrast, objective speech quality assessment methods (e.g., Perceptual Objective Listening Quality Analysis (POLQA)) do not take into account reverberation. Thus, they are not accurate and cannot be used to evaluate reverberant signals. 
       FIG. 3  illustrates example components for performing dereverberation according to examples of the present disclosure. As illustrated in  FIG. 3 , signals from two microphones  112   a / 112   b  are mapped to a subband domain by analysis filterbanks. For example, a first analysis filterbank  310  may convert a first microphone signal z 0 (n) in a time domain to a first microphone signal Z 0 (n, k) in a subband domain, while a second analysis filterbank  315  may convert a second microphone signal z 1 (n) in the time domain to a second microphone signal Z 1 (n, k) in the subband domain, where n is the frame index, k=0 to N/2 is the frequency index, and N is the number of subbands. 
     In some examples, the first analysis filterbank  310  and the second analysis filterbank  315  may include a uniform discrete Fourier transform (DFT) filterbank to convert the microphone signal z(n) from the time domain into the sub-band domain (e.g., converting to the frequency domain and then separating different frequency ranges into a plurality of individual sub-bands). Therefore, the audio signal Z may incorporate audio signals corresponding to multiple different microphones as well as different sub-bands (i.e., frequency ranges) as well as different frame indices (i.e., time ranges). Thus, the audio signal from the mth microphone may be represented as X m (n, k), where n denotes the frame index and k denotes the sub-band index. 
     To summarize  FIG. 3 , the first microphone signal Z 0 (n, k) and the second microphone signal Z 1 (n, k) may be used to estimate a coherence in each frequency index (e.g., frequency bin or subband), which is used to calculate coherence-to-diffuse ratio (CDR) values (e.g., CDR data). The CDR values may be used to derive a masking gain (e.g., DER gain values) to suppress late reverberations. The DER gain values are calculated with an over-subtraction factor to assure no suppression in a non-reverberant room. 
     As illustrated in  FIG. 3 , a first power spectral density (PSD) estimation component  320  may receive the first microphone signal Z 0 (n, k) and may generate a first PSD estimate, while a second PSD estimation component  325  may receive the second microphone signal Z 1 (n, k) and may generate a second PSD estimate. The PSD estimation components  320 / 325  may generate the PSD estimates using the following equation:
 
 S   x     i   [ n,k ]=(1−λ) S   x     i   [ n− 1, k ]+λ·| X   i [ m,k ]| 2   ,i= 0,1, k= 0 to  N/ 2  [1]
 
where λ∈(0, 1) denotes a forgetting factor and i is the microphone index.
 
     The cross-PSD estimation component  330  may receive the first microphone signal Z 0 (n, k) and the second microphone signal Z 1 (n, k) and may calculate a cross-PSD estimate using the following equation:
 
 S   x     0     x     1   [ n,k ]=(1−λ) S   x     0     x     1   [ n− 1, k ]+λ· X   0 [ m,k ] X   1 *[ m,k ]  [2]
 
     The first PSD estimation component  320  may send the first PSD estimate to an average component  335  and a coherence estimation component  340 . Similarly, the second PSD estimation component  325  may send the second PSD estimate to the average component  335  and the coherence estimation component  340 . The cross-PSD estimation component  330  may also send the cross-PSD estimate to the coherence estimation component  340 . The average component  335  may determine an average between the first PSD estimate and the second PSD estimate, which will be used to generate the output signal OUT(n, k). 
     The coherence estimation component  340  may receive the first PSD estimate, the second PSD estimate, and the cross-PSD estimate and may determine a coherence estimate using the equation below: 
                         Γ   x     ⁡     [     m   ,   k     ]       =         S       x   0     ⁢     x   1         ⁡     [     m   ,   k     ]               S     x   0       ⁡     [     m   ,   k     ]       ⁢       S     x   1       ⁡     [     m   ,   k     ]               ,     k   =     0   ⁢           ⁢   to   ⁢           ⁢     N   /   2                 [   3   ]               
where S x     0     x     1   [m, k] is the cross-PSD estimate, S x     0    is the first PSD estimate, and S x     1    is the second PSD estimate. The coherence estimation component  340  may send the coherence estimate to a coherence-to-diffuse ratio (CDR) estimation component  350 .
 
     The diffuse component specification component  345  may determine the coherence of diffuse components using the following equation: 
                         Γ   diff     ⁡     [   k   ]       =     sin   ⁢           ⁢     c   ⁡     (         2   ⁢   π   ⁢           ⁢     f   s     ⁢   d       N   ·   c       ⁢   k     )           ,     k   =     0   ⁢           ⁢   to   ⁢           ⁢     N   /   2                 [   4   ]               
where f s  is sampling frequency in Hertz (Hz), d is the distance between the sensors in meters (m), and c is the speed of sound in m/s.
 
     The diffuse component specification component  345  may send the coherence of diffuse components to the CDR estimation component  350 . Using the coherence estimate received from the coherence estimation component  340  and the coherence of diffuse components, the CDR estimation component  350  may generate a CDR estimate: 
     
       
         
           
             
               
                 
                   
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     The CDR estimation component  350  may send the CDR estimate to the gain calculation component  355 , which may calculate the gain in each band as: 
                       g   ⁡     [     m   ,   k     ]       =     max   ⁡     (       g   ⁢           ⁢   min     ,     1   -       μ       CDR   ⁡     [     m   ,   k     ]       +   1             )         ,     k   =     0   ⁢           ⁢   to   ⁢           ⁢     N   /   2                 [   6   ]               
where gmin is the minimum gain allowed and p is the over-subtraction factor.
 
     The multiplier component  360  may use these calculated gains to mask the subband coefficients from the first channel:
 
 X   0 ′[ m,k ]= g [ m,k ] X   0 [ m,k ]  [7]
 
representing the dereverberated signal. A synthesis filterbank  370  may convert this dereverberated signal in the subband domain back to time domain to generate output signal  375 .
 
       FIG. 4  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure. As illustrated in  FIG. 4 , in some examples the device  110  may perform dereverberation using an independent dereverberator  400 . 
     As described above with regard to  FIG. 3 , signals from two microphones  112   a / 112   b  are mapped to a subband domain by analysis filterbanks. For example, the first analysis filterbank  310  may convert the first microphone signal z 0 (n) in the time domain to the first microphone signal Z 0 (n, k) in the subband domain, while the second analysis filterbank  315  may convert the second microphone signal z 1 (n) in the time domain to the second microphone signal Z 1 (n, k) in the subband domain, where n is the frame index, k=0 to N/2 is the frequency index, and N is the number of subbands. 
     Similarly, a third analysis filterbank may convert a reference signal x(n) in the time domain to a reference signal X(n, k) in the subband domain. In some examples, the third analysis filterbank  410  may include a uniform discrete Fourier transform (DFT) filterbank to convert the reference signal x(n) from the time domain into the sub-band domain (e.g., converting to the frequency domain and then separating different frequency ranges into a plurality of individual sub-bands). Therefore, the audio signal X may incorporate reference audio signals corresponding to one or more loudspeakers  114  as well as different sub-bands (i.e., frequency ranges) as well as different frame indices (i.e., time ranges). Thus, the audio signal associated with the xth loudspeaker  114  may be represented as Xx(n, k), where n denotes the frame index and k denotes the sub-band index. While  FIG. 4  illustrates an example using a single reference channel, the disclosure is not limited thereto and the number of reference signals may vary without departing from the disclosure. 
     A first AEC component  120   a  may perform first echo cancellation (e.g., first AEC processing) to generate a first isolated signal M 0 (n, k). For example, the first AEC component  120   a  may generate an echo estimate  405  using the reference signal X(n, k) and may subtract the echo estimate  405  from the first microphone signal Z 0 (n, k) to generate the first isolated signal M 0 (n, k). If the echo estimate  405  corresponds to the echo signal Y(n, k) represented in the first microphone signal Z 0 (n, k), the first AEC component  120   a  may effectively remove the echo signal Y(n, k) and isolate the near-end speech S(n, k). The first isolated signal M 0 (n, k) generated by the first AEC component  120   a  may be output to the Residual Echo Suppressor (RES) component  122 , a noise estimator component  420 , and a dereverberation (DER) component  126 . The first AEC component  120   a  may also output the echo estimate  405  to the RES component  122 . 
     Similarly, a second AEC component  120   b  may perform second echo cancellation (e.g., second AEC processing) to generate a second isolated signal M 1 (n, k) using the reference signal X(n, k) and the second microphone signal Z 1 (n, k). However, the second AEC component  120   b  may only output the second isolated signal M 1 (n, k) to the DER component  126 . 
     The noise estimator component  420  may use the first isolated signal M 0 (n, k) to determine a noise estimate  425  and a signal-to-noise ratio (SNR) estimate  430 . The noise estimate  425  corresponds to an array of values (e.g., NoiseEstimate(n, k), such that a first noise estimate value corresponds to a first subband, a second noise estimate value corresponds to a second subband, and so on. In contrast, the SNR estimate  430  corresponds to a single SNR estimate value for an audio frame (e.g., SNR(n), such that the SNR estimate  430  does not change between subbands of the audio frame. The noise estimator  420  may send the noise estimate  425  to the noise component  124  and may send the SNR estimate  430  to the DER component  126 . 
     The device  110  may use the SNR estimate  430  to determine whether to perform DER processing. For example, if the SNR estimate  430  does not satisfy a condition (e.g., is below a threshold value δ, such as 10 dB), the device  110  may skip DER processing and prioritize Noise Reduction (NR) processing instead. However, if the SNR estimate  430  satisfies the condition (e.g., is above the threshold value δ), the device  110  may perform DER processing. 
     The DER component  126  may perform DER processing as described in greater detail above with regard to  FIG. 3 . For example, the DER component  126  may calculate CDR values using the first isolated signal M 0 (n, k) and the second isolated signal M 1 (n, k) and may use the CDR values to generate a DER estimate  435 . The DER estimate  435  may correspond to the DER gain values (e.g., DER gain data) described above. 
     The RES component  122  may perform residual echo suppression (RES) processing to the first isolated signal M 0 (n, k) to generate a first audio signal R RES (n, k). The RES component  122  may perform RES processing in order to suppress echo signals (or undesired audio) remaining in the first isolated signal M 0 (n, k). For example, the RES component  122  may calculate RES gains  415  based on the echo estimate  405  in order to apply additional attenuation. To illustrate an example, the RES component  122  may use the echo estimate  405  and/or the first isolated signal M 0 (n, k) to identify first subbands in which the first AEC component  120   a  applied attenuation. The RES component  122  may then determine whether there are residual echo components represented in the first subbands of the first isolated signal M 0 (n, k) and may calculate the RES gains  415  to perform residual echo suppression processing. For example, the RES component  122  may apply the RES gains  415  to the first isolated signal M 0 (n, k) in order to generate the first audio signal R RES (n, k). 
     In some examples, the RES component  122  may vary an amount of RES processing based on current conditions, although the disclosure is not limited thereto. Additionally or alternatively, the RES component  122  may perform RES processing differently based on individual frequency indexes. For example, the RES component  122  may control an amount of gain applied to low frequency bands, which are commonly associated with speech. The RES component  122  may output the first audio signal R RES (n, k) and RES gains  415 . 
     As discussed above, the device  110  may determine whether to perform DER processing based on the SNR estimate  430 . If the device  110  determines to perform DER processing (e.g., SNR&gt;6), a multiplier component  440  may receive the first audio signal R RES (n, k) and the DER estimate  435  generated by the DER component  126  and may generate a second audio signal R DER (n, k). For example, the multiplier component  440  may multiply the first audio signal R RES (n, k) by the DER estimate  435  for individual frequency indexes to generate the second audio signal R DER (n, k). In this example, the multiplier component  440  may output the second audio signal R DER (n, k) to the noise component  124  and to a noise estimator component  445 . 
     If the SNR estimate  430  satisfies the condition (e.g., is above the threshold value), the device  110  may perform DER processing the noise estimator component  445  may be configured to determine an updated noise estimate. For example, the noise estimator component  445  may generate a DER noise estimate  450  based on the second audio signal R DER (n, k) (e.g., after applying the DER gain values). Similar to the noise estimate  425  described above, the DER noise estimate  450  corresponds to an array of values (e.g., NoiseEstimate(n, k), such that a first noise estimate value corresponds to a first subband, a second noise estimate value corresponds to a second subband, and so on. The device  110  may use the DER noise estimate  450  to perform NR processing, as described in greater detail below, to avoid over suppressing the noise. For example, as DER processing removes some diffuse noise, the original noise estimate  425  will be higher than the DER noise estimate  450 , resulting in more aggressive NR processing. 
     If the device  110  determines not to perform DER processing (e.g., SNR&lt;δ), the multiplier component  440  may effectively pass the first audio signal R RES (n, k) to the noise component  124  without applying the DER estimate  435 . In this example, the noise estimator  445  does not generate the DER noise estimate  450  and the noise component  124  performs NR processing using the original noise estimate  425 . 
     The noise component  124  may be configured to perform NR processing to generate an output signal OUT(n, k) in the subband domain. For example, if the device  110  determines not to perform DER processing (e.g., SNR&lt;δ), the noise component  124  may perform NR processing to the first audio signal R RES (n, k) using the noise estimate  425 . In contrast, if the device  110  determines to perform DER processing (e.g., SNR&gt;δ), the noise component  124  may perform NR processing to the second audio signal R DER (n, k) using the DER noise estimate  450  received from the noise estimator component  445 . Thus, the noise component  124  may control an amount of NR processing differently depending on whether the device  110  performs DER processing or not. 
     As illustrated in  FIG. 4 , the noise component  124  may include a comfort noise generator component  460  and/or a noise reducer component  465 . The comfort noise generator component  460  and/or the noise reducer component  465  may use either the noise estimate  425  (e.g., SNR&lt;δ) or the DER noise estimate  450  (e.g., SNR&gt;δ) to generate the output signal OUT(n, k). 
     As illustrated in  FIG. 4 , the noise component  124  may generate the output signal OUT(n, k) and send the output signal OUT(n, k) to the synthesis filterbank  470 . The synthesis filterbank  470  may receive the RES gains  415  from the RES component  122  and the output signal OUT(n, k) from the noise component  124 . The output signal OUT(n, k) may be in the subband domain and the synthesis filterbank  470  may convert the output signal OUT(n, k) from the subband domain to the time domain to generate output signal out(t)  475 . For example, the output signal OUT(n, k) in the subband domain may include a plurality of separate sub-bands (e.g., individual frequency bands) and the synthesis filterbank  470  may combine the plurality of subbands to generate the output signal out(t)  475  in the time domain. 
     While not illustrated in  FIG. 4 , in some examples the device  110  may include adaptive gain control (AGC) (not illustrated) and/or dynamic range compression (DRC) (not illustrated) (which may also be referred to as dynamic range control) to generate the output signal without departing from the disclosure. The device  110  may apply the noise reduction, the AGC, and/or the DRC using any techniques known to one of skill in the art. In some examples, the device  110  may perform additional processing in the time domain using the RES gain values  415 , although the disclosure is not limited thereto. For example, the device  110  may use the RES gain values  415  to estimate an amount of noise represented in the output signal and perform additional processing based on the estimated amount of noise. 
       FIG. 5  illustrates a chart representing reduction in reverberation according to examples of the present disclosure. The speech to reverberation modulation ratio (SRMR) chart  510  represents a magnitude of SRMR values for different configurations at different reverberation time values corresponding to 60 dB drop (e.g., RT60 values). Thus, the horizontal axis (e.g., x axis) indicates a RT60 value, while the vertical axis (e.g., y axis) indicates a corresponding SRMR value. 
     As illustrated in  FIG. 5 , the SRMR chart  510  includes simulations corresponding to six different configurations. The SRMR score improved in most of the simulations, with the SRMR chart  510  representing the evaluation for a single talk example at 20 dB SNR for three different speech levels. For example, the solid black line (with diamonds) represents the dereverberated signal at 60 dB, whereas the dashed black line (with diamonds) represents the reverberated signal at 60 dB (e.g., bypassing DER processing). Similarly, the solid gray line (with circles) represents the dereverberated signal at 70 dB, whereas the dashed gray line (with circles) represents the reverberated signal at 70 dB (e.g., bypassing DER processing). Finally, the solid gray line (with squares) represents the dereverberated signal at 80 dB, whereas the dashed gray line (with squares) represents the reverberated signal at 80 dB (e.g., bypassing DER processing). 
       FIG. 6  is a flowchart conceptually illustrating an example method for performing dereverberation according to embodiments of the present disclosure. As illustrated in  FIG. 6 , the device  110  may convert ( 610 ) a first microphone signal from a time domain to a subband domain and may convert ( 612 ) a second microphone signal from the time domain to the subband domain. For example, as described above with regard to  FIG. 3 , the first analysis filterbank  310  may convert the first microphone signal z 0 (n) in the time domain to the first microphone signal Z 0 (n, k) in the subband domain, while the second analysis filterbank  315  may convert the second microphone signal z 1 (n) in the time domain to the second microphone signal Z 1 (n, k) in the subband domain, where n is the frame index, k=0 to N/2 is the frequency index, and N is the number of subbands. 
     The device  110  may estimate ( 614 ) a first power spectral density (PSD) function associated with the first microphone signal and may estimate ( 616 ) a second PSD function associated with the second microphone signal. For example, the PSD functions may describe a power present in the first and second microphone signals as a function of frequency or subband. The device  110  may estimate the PSD functions using Equation [1] described above. For example, the first power spectral density (PSD) estimation component  320  may receive the first microphone signal Z 0 (n, k) and may generate the first PSD function, while the second PSD estimation component  325  may receive the second microphone signal Z 1 (n, k) and may generate the second PSD function. 
     The device  110  may estimate ( 618 ) a cross power spectral density (CPSD) function using the first microphone signal and the second microphone signal. For example, the cross-PSD estimation component  330  may receive the first microphone signal Z 0 (n, k) and the second microphone signal Z 1 (n, k) and may calculate the cross-PSD function using Equation [2] described above. 
     The device  110  may calculate ( 620 ) coherence estimate values using the first PSD function, the second PSD function, and the CPSD function. For example, the coherence estimation component  340  may receive the first PSD function, the second PSD function, and the cross-PSD function and may determine a coherence estimate using Equation [3] described above. The device  110  may determine ( 622 ) a coherence estimate of the diffuse components. For example, the diffuse component specification component  345  may determine the coherence of diffuse components using Equation [4] described above. 
     The device  110  may estimate ( 624 ) coherence-to-diffuse ratio (CDR) values using the coherence estimate values and the coherence estimate of the diffuse components. For example, the CDR estimation component  350  may generate the CDR values using the coherence estimate and the coherence of diffuse components, as described above with regard to Equation [5]. The device  110  may then determine ( 626 ) gain values using the CDR values. For example, the gain calculation component  355  may calculate the gain in each band using Equation [6] described above. 
     The device  110  may determine ( 628 ) an average PSD function using the first PSD function and the second PSD function. For example, the average component  335  may determine an average between the first PSD function and the second PSD function, and the device  110  may use the average PSD function to generate the output signal. Thus, the device  110  may multiply ( 630 ) the average PSD function by the gain values to generate a first output signal in the subband domain, and may generate ( 632 ) a second output signal in the time domain. For example, the multiplier component  360  may use the gain values to mask the subband coefficients from the average PSD function. The synthesis filterbank  370  may convert the first output signal in the subband domain to the second output signal in the time domain. 
       FIG. 7  is a flowchart conceptually illustrating an example method for performing dereverberation within a voice processing pipeline according to embodiments of the present disclosure. As illustrated in  FIG. 7 , the device  110  may convert ( 710 ) a first microphone signal from the time domain to the subband domain and may convert ( 712 ) a second microphone signal from the time domain to the subband domain. For example, the first analysis filterbank  310  may convert the first microphone signal z 0 (n) in the time domain to the first microphone signal Z 0 (n, k) in the subband domain, while the second analysis filterbank  315  may convert the second microphone signal z 1 (n) in the time domain to the second microphone signal Z 1 (n, k) in the subband domain, where n is the frame index, k=0 to N/2 is the frequency index, and N is the number of subbands. The device  110  may then convert ( 714 ) a reference signal from the time domain to the subband domain. For example, the third analysis filterbank  410  may convert the reference signal x(n) in the time domain to the reference signal X(n, k) in the subband domain. In some examples, the third analysis filterbank  410  may include a uniform discrete Fourier transform (DFT) filterbank to convert the reference signal x(n) from the time domain into the sub-band domain (e.g., converting to the frequency domain and then separating different frequency ranges into a plurality of individual sub-bands). Therefore, the audio signal X may incorporate reference audio signals corresponding to one or more loudspeakers  114  as well as different sub-bands (i.e., frequency ranges) as well as different frame indices (i.e., time ranges). Thus, the audio signal associated with the xth loudspeaker  114  may be represented as Xx(n, k), where n denotes the frame index and k denotes the sub-band index. 
     The device  110  may perform ( 716 ) first echo cancellation using the first microphone signal and the reference signal to generate a first isolated signal and may perform ( 718 ) second echo cancellation using the second microphone signal and the reference signal to generate a second isolated signal. For example, the first AEC component  120   a  may perform first echo cancellation (e.g., first AEC processing) to generate a first isolated signal M 0 (n, k) by subtracting the reference signal X(n, k) from the first microphone signal Z 0 (n, k). Similarly, the second AEC component  120   b  may perform second echo cancellation (e.g., second AEC processing) to generate a second isolated signal M 1 (n, k) by subtracting the reference signal X(n, k) from the second microphone signal Z 1 (n, k). 
     The device  110  may determine ( 720 ) a noise estimate. For example, the noise estimator component  420  may use the first isolated signal M 0 (n, k) to determine a noise estimate  425  and a signal-to-noise ratio (SNR) estimate  430 . The device  110  may determine ( 722 ) coherence-to-diffuse ratio (CDR) values and determine ( 724 ) DER gain values using the CDR values. For example, the DER component  126  may calculate CDR values using the first isolated signal M 0 (n, k) and the second isolated signal M 1 (n, k) and may use the CDR values to generate a DER estimate  435 , as described in greater detail above with regard to  FIG. 3 . 
     The device  110  may perform ( 726 ) residual echo suppression using the RES component  122  to generate a first audio signal R RES (n, k). For example, the RES component  122  may perform RES processing in order to suppress echo signals (or undesired audio) remaining in the first isolated signal M 0 (n, k). In some examples, the RES component  122  may vary an amount of RES processing based on current conditions, although the disclosure is not limited thereto. Additionally or alternatively, the RES component  122  may perform RES processing differently based on individual frequency indexes. For example, the RES component  122  may control an amount of gain applied to low frequency bands, which are commonly associated with speech. 
     The device  110  may perform ( 728 ) dereverberation processing using the DER gain values to generate a second audio signal R DER (n, k). For example, the multiplier component  440  may receive the first audio signal R RES (n, k) and the DER estimate  435  generated by the DER component  126  and may generate the second audio signal R DER (n, k). Thus, the multiplier component  440  may multiply the first audio signal R RES (n, k) by the DER estimate  435  for individual frequency indexes to generate the second audio signal R DER (n, k). 
     After performing dereverberation processing, the device  110  may perform ( 730 ) noise reduction using a noise estimate. In some examples, the device  110  may use the first noise estimate determined in step  720 . However, the disclosure is not limited thereto, and in other examples the device  110  may determine a second noise estimate as part of step  728  (e.g., after performing dereverberation processing) and the device  110  may perform noise reduction using the second noise estimate. For example, the noise estimator component  445  may be configured to determine a DER noise estimate  450  (e.g., second noise estimate) based on the second audio signal R DER (n, k) (e.g., after applying the DER gain values). The device  110  may use the DER noise estimate  450  to perform NR processing in order to avoid over suppressing the noise. For example, as the dereverberation processing removes some diffuse noise, the original noise estimate  425  (e.g., first noise estimate) will be higher than the DER noise estimate  450  (e.g., second noise estimate), resulting in more aggressive NR processing. 
       FIG. 8  illustrates multiple configurations of the reverberation components within the voice processing pipeline according to embodiments of the present disclosure. As illustrated in  FIG. 8 , an audio pipeline  810  may include three major components; the AEC component  120  configured to perform AEC processing to perform echo cancellation, the RES component  122  configured to perform RES processing to suppress a residual echo signal, and the noise component  124  configured to perform NR processing to attenuate a noise signal. As described above, the device  110  may perform dereverberation by including the DER component  126 , which may be configured to perform DER processing to reduce and/or remove reverberation in the audio pipeline  810 . 
     As illustrated in  FIG. 8 , performing dereverberation processing may correspond to three separate stages, which can be implemented at different points throughout the audio pipeline  810 . For example, the device  110  may determine ( 820 ) DER gains in a first stage, may apply ( 830 ) the DER gains in a second stage, and may determine ( 840 ) a noise estimate corresponding to noise components of the signal in a third stage. 
     In some examples, the first stage of determining the DER gains in step  820  may correspond to the device  110  being configured to determine ( 722 ) the coherence-to-diffuse ratio (CDR) values and determine ( 724 ) gain values using the CDR values, as described above with regard to  FIG. 7 . The system  100  can determine these gain values either before performing echo cancellation (e.g., before AEC  822 ) or after performing echo cancellation (e.g., after AEC  824 ). Examples of determining the DER gains before the AEC component  120  are illustrated in  FIGS. 13-14 , while examples of determining the DER gains after the AEC component  120  are illustrated in  FIGS. 4 and 10-12 . 
     In some examples, the second stage of applying the DER gains in step  830  may correspond to the device  110  being configured to perform ( 728 ) dereverberation processing using the gain values, as described above with regard to  FIG. 7 . The system  100  can apply the gain values at four different points in the audio pipeline  810 , such as before performing echo cancellation (e.g., before AEC  832 ), after performing echo cancellation (e.g., after AEC  834 ), after performing residual echo suppression (e.g., after RES  836 ), or during noise reduction (e.g., during NR  838 ). Examples of these different implementations are illustrated in  FIGS. 4 and 10-14 . 
     In some examples, the third stage of determining the noise estimate in step  840  may correspond to the device  110  being configured to determine ( 720 ) the noise estimate, as described above with regard to  FIG. 7 . The system  100  can determine the noise estimate at two different points in the audio pipeline  810 , such as after performing echo cancellation (e.g., after AEC  842 ) or after performing dereverberation processing (e.g., after DER  844 ). Determining the noise estimate after performing dereverberation processing may be beneficial as dereverberation processing may suppress or attenuate some of the noise components of the audio signal. Thus, determining the noise estimate after performing the dereverberation processing may reduce redundant noise suppression that would occur if the noise component  124  further attenuated portions of the noise signal that were already attenuated by the dereverberation processing. An example of determining the noise estimate after performing dereverberation processing is illustrated in  FIG. 4 , while examples of determining the noise estimate after the AEC component  120  are illustrated in  FIGS. 10-14 . 
       FIG. 9  is a flowchart conceptually illustrating an example method for performing dereverberation within a voice processing pipeline according to embodiments of the present disclosure. As illustrated in  FIG. 9 , the device  110  may perform ( 716 ) first echo cancellation using the first microphone signal and the reference signal to generate a first isolated signal and may perform ( 718 ) second echo cancellation using the second microphone signal and the reference signal to generate a second isolated signal, as described in greater detail above with regard to  FIG. 7 . 
     The device  110  may perform ( 910 ) residual echo suppression (RES) processing on the first isolated signal to generate a RES output signal. For example, the RES component  122  may perform residual echo suppression (RES) processing to the first isolated signal M 0 (n, k) to generate the first audio signal R RES (n, k) (e.g., RES output signal). The RES component  122  may perform RES processing in order to suppress echo signals (or undesired audio) remaining in the first isolated signal M 0 (n, k). As part of performing RES processing, the device  110  may determine ( 912 ) RES gain values corresponding to the RES processing. 
     The device  110  may determine ( 914 ) a first noise estimate and may calculate ( 916 ) a signal-to-noise-ratio (SNR) estimate using the first noise estimate. For example, the noise estimator component  420  may use the first isolated signal M 0 (n, k) to determine a noise estimate  425  and a signal-to-noise ratio (SNR) estimate  430 . 
     The device  110  may determine whether the SNR estimate is above a threshold value S. If the SNR estimate is below the threshold value δ, the device  110  may perform ( 920 ) noise reduction on the RES output signal using the first noise estimate to generate an output signal OUT(n, k). For example, the device  110  may skip the dereverberation processing and apply normal noise reduction using the first noise estimate determined in step  914 . 
     If the SNR estimate is above the threshold value δ, however, the device  110  may determine ( 922 ) coherence-to-diffuse ratio (CDR) values using the first and second isolated signals, may determine ( 924 ) DER gain values using the CDR values, and may apply ( 926 ) the DER gain values to the RES output signal to generate a dereverberated signal. For example, the DER component  126  may calculate CDR values using the first isolated signal M 0 (n, k) and the second isolated signal M 1 (n, k), may use the CDR values to generate a DER estimate  435 , and may apply the DER estimate  435  to the RES output signal generated by the RES component  122 . As described above with regard to  FIG. 4 , the multiplier component  440  may receive the first audio signal R RES (n, k) and the DER estimate  435  generated by the DER component  126  and may generate the second audio signal R DER (n, k). Thus, the multiplier component  440  may multiply the first audio signal R RES (n, k) by the DER estimate  435  for individual frequency indexes to generate the second audio signal R DER (n, k). 
     After performing dereverberation processing, the device  110  may determine ( 928 ) a second noise estimate using the dereverberated signal (e.g., second audio signal R DER (n, k)) and may perform ( 930 ) noise reduction on the dereverberated signal using the second noise estimate to generate a first output signal OUT(n, k). For example, the noise estimator component  445  may be configured to determine a DER noise estimate  450  (e.g., second noise estimate) based on the second audio signal R DER (n, k) (e.g., after applying the DER gain values). The device  110  may use the DER noise estimate  450  to perform NR processing in order to avoid over suppressing the noise. For example, as the dereverberation processing removes some diffuse noise, the original noise estimate  425  (e.g., first noise estimate) will be higher than the DER noise estimate  450  (e.g., second noise estimate), resulting in more aggressive NR processing. 
     After generating the first output signal OUT(n, k) in the subband domain, the device  110  may convert ( 932 ) the first output signal OUT(n, k) from the subband domain to the time domain to generate a second output signal out(t). In some examples, the device  110  may perform additional processing to the output signal out(t) in the time domain without departing from the disclosure. For example, the device  110  may perform adaptive gain control (AGC), dynamic range compression (DRC) (which may also be referred to as dynamic range control), and/or the like without departing from the disclosure. In some examples, the device  110  may perform the additional processing in the time domain using the RES gain values  415 , although the disclosure is not limited thereto. For example, the device  110  may use the RES gain values  415  to estimate an amount of noise represented in the output signal and perform additional processing based on the estimated amount of noise. 
       FIG. 10  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure. For example,  FIG. 10  illustrates an example of a combined dereverberator  1000  in which the noise component  124  may be configured to perform noise reduction and/or dereverberation at the same time. As most of the components illustrated in  FIG. 10  are described above with regard to  FIG. 4 , a redundant description is omitted. 
     As illustrated in  FIG. 10 , the DER component  126  may calculate a DER estimate  1035  based on the first and second isolated signals generated by the AEC components  120   a / 120   b , similar to how the DER component  126  calculates the DER estimate  435  as described above with regard to  FIG. 4 . However, instead of applying the DER gain values to the output of the RES component  122 , prior to the noise component  124 ,  FIG. 10  illustrates an example in which the DER component  126  may send the DER estimate  1035  (e.g., DER gain values) to the noise component  124 . Thus, the noise component  124  may be configured to perform a combination of noise reduction and/or dereverberation processing. 
     In the combined dereverberator  1000  example illustrated in  FIG. 10 , the noise component  124  may determine noise reduction (NR) gain values using the noise estimate  425 , similar to how the noise component  124  typically performs noise reduction processing. However, the noise component  124  may be configured to select the smaller of the DER gain values and the NR gain values with which to perform noise reduction processing. For example, for an individual subband, the noise component  124  may identify the lower value between a DER gain value and a NR gain value and perform NR processing using the lower value. Thus, the noise component  124  does not perform redundant noise suppression using both the DER gain value and the NR gain value, but instead performs a single step of noise suppression using one of the two values. To illustrate an example, if the environment (e.g., room) around the device  110  is noisy, the noise component  124  will ignore the DER gain value and select the NR gain value, which will result in greater noise reduction than the DER gain value. Similarly, if the environment is not noisy, the noise component  124  may ignore the NR gain value and select the DER gain value, which will result in greater noise reduction than the NR gain value. 
       FIG. 11  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure.  FIG. 11  illustrates an example of a pre-RES dereverberator  1100 , which performs dereverberation processing prior to performing residual echo suppression processing. As most of the components illustrated in  FIG. 11  are described above with regard to  FIG. 4 , a redundant description is omitted. 
     As illustrated in  FIG. 11 , the DER component  126  may calculate a DER estimate  1135  based on the first and second isolated signals generated by the AEC components  120   a / 120   b , similar to how the DER component  126  calculates the DER estimate  435  as described above with regard to  FIG. 4 . However, instead of applying the DER gain values to the output of the RES component  122 ,  FIG. 11  illustrates an example in which the DER gain values are applied prior to the RES component  122 . 
     As illustrated in the pre-RES dereveberator  1100  example shown in  FIG. 11 , the DER component  126  may send the DER estimate  1135  (e.g., DER gain values) to a multiplier component  1110  that is located between the first AEC component  120   a  and the RES component  122 . The multiplier component  1110  may receive the first isolated signal M 0 (n, k) and the DER estimate  1135  generated by the DER component  126  and may generate a first audio signal (e.g., dereverberated audio signal) R DER (n, k). For example, the multiplier component  1110  may multiply the first isolated signal M 0 (n, k) by the DER estimate  1135  for individual frequency indexes to generate the first audio signal R DER (n, k). 
     In the pre-RES dereverberator  1100  example, the multiplier component  1110  may output the first audio signal R DER (n, k) to the RES component  122  and the RES component  122  may perform RES processing on the first audio signal R DER (n, k) to generate a second audio signal R RES (n, k). The RES component  122  may perform RES processing in order to suppress echo signals (or undesired audio) remaining in the first audio signal R DER (n, k), as described in greater detail above with regard to  FIG. 4 . 
       FIG. 12  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure.  FIG. 12  illustrates an example of a post-RES dereverberator  1200 , which performs dereverberation processing after performing residual echo suppression processing. As most of the components illustrated in  FIG. 12  are described above with regard to  FIG. 4 , a redundant description is omitted. 
     As illustrated in  FIG. 12 , the DER component  126  may calculate a DER estimate  1235  based on the first and second isolated signals generated by the AEC components  120   a / 120   b , similar to how the DER component  126  calculates the DER estimate  435  as described above with regard to  FIG. 4 . However, instead of applying the DER gain values prior to performing RES processing by the RES component  122 , as illustrated in the pre-RES dereveberator  1100  example shown in  FIG. 11 , the post-RES dereverberator  1200  example illustrated in  FIG. 12  applies the DER estimate  1235  to the output of the RES component  122 . 
     As illustrated in the post-RES dereverberator  1200  example shown in  FIG. 12 , the DER component  126  may send the DER estimate  1235  (e.g., DER gain values) to the RES component  122 . The RES component  122  may perform RES processing on the first isolated signal M 0 (n, k) output by the first AEC component  120   a  to generate a first audio signal R RES (n, k). The RES component  122  may perform RES processing in order to suppress echo signals (or undesired audio) remaining in the first isolated signal M 0 (n, k), as described in greater detail above with regard to  FIG. 4 . 
     After performing the RES processing to generate the first audio signal R RES (n, k), the RES component  122  may apply the DER estimate  1235  generated by the DER component  126  to the first audio signal R RES (n, k) to generate a second audio signal (e.g., dereverberated audio signal) R DER (n, k). For example, the RES component  122  may multiply the first audio signal R RES (n, k) by the DER estimate  1235  for individual frequency indexes to generate the second audio signal R DER (n, k). While not illustrated in  FIG. 12 , in some examples the RES component  122  may include a multiplier component and may generate the second audio signal R DER (n, k) as described above with regard to  FIG. 4  without departing from the disclosure. The RES component  122  may then output the second audio signal R DER (n, k) to the noise component  124  to perform NR processing to generate an output signal OUT(n, k), as described in greater detail above with regard to  FIG. 4 . 
       FIG. 13  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure.  FIG. 13  illustrates an example of a pre-AEC dereverberator  1300 , which performs dereverberation processing prior to performing echo cancellation processing using the AEC component  120 . As most of the components illustrated in  FIG. 13  are described above with regard to  FIG. 4 , a redundant description is omitted. 
     As illustrated in  FIG. 13 , the DER component  126  may calculate a DER estimate  1335  prior to the first AEC component  120   a  performing echo cancellation. For example, the DER component  126  may calculate the DER estimate  1335  based on the first microphone signal Z 0 (n, k) and the second microphone signal Z 1 (n, k) in the subband domain. 
     In the pre-AEC reverberator  1300  example illustrated in  FIG. 13 , the device  110  may also apply the DER estimate  1335  prior to the AEC component  120 . For example, the DER component  126  may send the DER estimate  1335  to a multiplier component  1310  and the multiplier component  1310  may multiply the first microphone signal Z 0 (n, k) by the DER estimate  1335  to generate a dereverberated microphone signal Z 0DER (n, k). The multiplier component  1310  may then send the dereverberated microphone signal Z 0DER (n, k) to the first AEC component  120   a  and the first AEC component  120   a  may perform echo cancellation to the dereverberated microphone signal Z 0DER (n, k) in order to generate the first isolated signal M 0 (n, k). 
       FIG. 14  illustrates example components for performing dereverberation within a voice processing pipeline according to examples of the present disclosure.  FIG. 14  illustrates an example of pre-AEC dereverberator estimation  1400 , which determines DER gain values prior to performing echo cancellation using the AEC component  120 . In contrast to the pre-AEC dereverberator  1300  example illustrated in  FIG. 13 , however,  FIG. 14  illustrates an example in which the device  110  performs dereverberation processing (e.g., applies the DER gain values) after performing residual echo suppression processing. As most of the components illustrated in  FIG. 10  are described above with regard to  FIG. 4 , a redundant description is omitted. 
     As illustrated in  FIG. 14 , the DER component  126  may calculate a DER estimate  1435  prior to the first AEC component  120   a  performing echo cancellation. For example, the DER component  126  may calculate the DER estimate  1435  based on the first microphone signal Z 0 (n, k) and the second microphone signal Z 1 (n, k) in the subband domain. In the pre-AEC dereverberator estimation  1400  example illustrated in  FIG. 14 , however, the device  110  does not apply the DER estimate  1435  until after the AEC component  120  performs echo cancellation. For example, the pre-AEC dereverberator estimation  1400  example illustrated in  FIG. 14  applies the DER estimate  1435  to the output of the RES component  122 . 
     As illustrated in the pre-AEC dereverberator estimation  1400  example shown in  FIG. 14 , the DER component  126  may send the DER estimate  1435  (e.g., DER gain values) to the RES component  122 . The first AEC component  120   a  may perform echo cancellation on the first microphone signal Z 0 (n, k) and the reference signal X(n, k) to generate the first isolated signal M 0 (n, k), as described in greater detail above with regard to  FIG. 4 . The RES component  122  may perform RES processing on the first isolated signal M 0 (n, k) output by the first AEC component  120   a  to generate a first audio signal R RES (n, k). The RES component  122  may perform RES processing in order to suppress echo signals (or undesired audio) remaining in the first isolated signal M 0 (n, k), as described in greater detail above with regard to  FIG. 4 . 
     After performing the RES processing to generate the first audio signal R RES (n, k), the RES component  122  may apply the DER estimate  1435  generated by the DER component  126  to the first audio signal R RES (n, k) to generate a second audio signal R DER (n, k) (e.g., dereverberated audio signal). For example, the RES component  122  may multiply the first audio signal R RES (n, k) by the DER estimate  1435  for individual frequency indexes to generate the second audio signal R DER (n, k). While not illustrated in  FIG. 14 , in some examples the RES component  122  may include a multiplier component and may generate the second audio signal R DER (n, k) as described above with regard to  FIG. 4  without departing from the disclosure. The RES component  122  may then output the second audio signal R DER (n, k) to the noise component  124  to perform NR processing to generate an output signal OUT(n, k), as described in greater detail above with regard to  FIG. 4 . 
       FIG. 15  is a block diagram conceptually illustrating example components of a system according to embodiments of the present disclosure. In operation, the system  100  may include computer-readable and computer-executable instructions that reside on the device  110 , as will be discussed further below. 
     The device  110  may include one or more audio capture device(s), such as a microphone array which may include one or more microphones  112 . The audio capture device(s) may be integrated into a single device or may be separate. The device  110  may also include an audio output device for producing sound, such as loudspeaker(s)  114 . The audio output device may be integrated into a single device or may be separate. 
     As illustrated in  FIG. 15 , the device  110  may include an address/data bus  1524  for conveying data among components of the device  110 . Each component within the device  110  may also be directly connected to other components in addition to (or instead of) being connected to other components across the bus  1524 . 
     The device  110  may include one or more controllers/processors  1504 , which may each include a central processing unit (CPU) for processing data and computer-readable instructions, and a memory  1506  for storing data and instructions. The memory  1506  may include volatile random access memory (RAM), non-volatile read only memory (ROM), non-volatile magnetoresistive (MRAM) and/or other types of memory. The device  110  may also include a data storage component  1508 , for storing data and controller/processor-executable instructions (e.g., instructions to perform operations discussed herein). The data storage component  1508  may include one or more non-volatile storage types such as magnetic storage, optical storage, solid-state storage, etc. The device  110  may also be connected to removable or external non-volatile memory and/or storage (such as a removable memory card, memory key drive, networked storage, etc.) through the input/output device interfaces  1502 . 
     The device  110  includes input/output device interfaces  1502 . A variety of components may be connected through the input/output device interfaces  1502 . For example, the device  110  may include one or more microphone(s)  112  (e.g., a plurality of microphone(s)  112  in a microphone array), one or more loudspeaker(s)  114 , and/or a media source such as a digital media player (not illustrated) that connect through the input/output device interfaces  1502 , although the disclosure is not limited thereto. Instead, the number of microphone(s)  112  and/or the number of loudspeaker(s)  114  may vary without departing from the disclosure. In some examples, the microphone(s)  112  and/or loudspeaker(s)  114  may be external to the device  110 , although the disclosure is not limited thereto. The input/output interfaces  1502  may include A/D converters (not illustrated) and/or D/A converters (not illustrated). 
     The input/output device interfaces  1502  may also include an interface for an external peripheral device connection such as universal serial bus (USB), FireWire, Thunderbolt, Ethernet port or other connection protocol that may connect to network(s)  199 . 
     The input/output device interfaces  1502  may be configured to operate with network(s)  199 , for example via an Ethernet port, a wireless local area network (WLAN) (such as WiFi), Bluetooth, ZigBee and/or wireless networks, such as a Long Term Evolution (LTE) network, WiMAX network, 3G network, etc. The network(s)  199  may include a local or private network or may include a wide network such as the internet. Devices may be connected to the network(s)  199  through either wired or wireless connections. 
     The device  110  may include components that may comprise processor-executable instructions stored in storage  1508  to be executed by controller(s)/processor(s)  1504  (e.g., software, firmware, hardware, or some combination thereof). For example, components of the device  110  may be part of a software application running in the foreground and/or background on the device  110 . Some or all of the controllers/components of the device  110  may be executable instructions that may be embedded in hardware or firmware in addition to, or instead of, software. In one embodiment, the device  110  may operate using an Android operating system (such as Android 4.3 Jelly Bean, Android 4.4 KitKat or the like), an Amazon operating system (such as FireOS or the like), or any other suitable operating system. 
     Computer instructions for operating the device  110  and its various components may be executed by the controller(s)/processor(s)  1504 , using the memory  1506  as temporary “working” storage at runtime. The computer instructions may be stored in a non-transitory manner in non-volatile memory  1506 , storage  1508 , or an external device. Alternatively, some or all of the executable instructions may be embedded in hardware or firmware in addition to or instead of software. 
     Multiple devices may be employed in a single device  110 . In such a multi-device device, each of the devices may include different components for performing different aspects of the processes discussed above. The multiple devices may include overlapping components. The components listed in any of the figures herein are exemplary, and may be included a stand-alone device or may be included, in whole or in part, as a component of a larger device or system. 
     The concepts disclosed herein may be applied within a number of different devices and computer systems, including, for example, general-purpose computing systems, server-client computing systems, mainframe computing systems, telephone computing systems, laptop computers, cellular phones, personal digital assistants (PDAs), tablet computers, video capturing devices, wearable computing devices (watches, glasses, etc.), other mobile devices, video game consoles, speech processing systems, distributed computing environments, etc. Thus the components, components and/or processes described above may be combined or rearranged without departing from the ope of the present disclosure. The functionality of any component described above may be allocated among multiple components, or combined with a different component. As discussed above, any or all of the components may be embodied in one or more general-purpose microprocessors, or in one or more special-purpose digital signal processors or other dedicated microprocessing hardware. One or more components may also be embodied in software implemented by a processing unit. Further, one or more of the components may be omitted from the processes entirely. 
     The above embodiments of the present disclosure are meant to be illustrative. They were chosen to explain the principles and application of the disclosure and are not intended to be exhaustive or to limit the disclosure. Many modifications and variations of the disclosed embodiments may be apparent to those of skill in the art. Persons having ordinary skill in the field of computers and/or digital imaging should recognize that components and process steps described herein may be interchangeable with other components or steps, or combinations of components or steps, and still achieve the benefits and advantages of the present disclosure. Moreover, it should be apparent to one skilled in the art, that the disclosure may be practiced without some or all of the specific details and steps disclosed herein. 
     Aspects of the disclosed system may be implemented as a computer method or as an article of manufacture such as a memory device or non-transitory computer readable storage medium. The computer readable storage medium may be readable by a computer and may comprise instructions for causing a computer or other device to perform processes described in the present disclosure. The computer readable storage medium may be implemented by a volatile computer memory, non-volatile computer memory, hard drive, solid-state memory, flash drive, removable disk and/or other media. Some or all of the fixed beamformer, acoustic echo canceller (AEC), adaptive noise canceller (ANC) unit, residual echo suppression (RES), double-talk detector, etc. may be implemented by a digital signal processor (DSP). 
     Embodiments of the present disclosure may be performed in different forms of software, firmware and/or hardware. Further, the teachings of the disclosure may be performed by an application specific integrated circuit (ASIC), field programmable gate array (FPGA), or other component, for example. 
     Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list. 
     Conjunctive language such as the phrase “at least one of X, Y and Z,” unless specifically stated otherwise, is to be understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z, or a combination thereof. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y and at least one of Z to each is present. 
     As used in this disclosure, the term “a” or “one” may include one or more items unless specifically stated otherwise. Further, the phrase “based on” is intended to mean “based at least in part on” unless specifically stated otherwise.