阅读说明：本技术 风噪声检测系统和方法 (Wind noise detection system and method ) 是由 芮立扬 G·肯南 于 2020-04-28 设计创作，主要内容包括：系统和方法包括：风检测器,其用于接收音频输入信号并输出风检测标志,该风检测标志包括单通道风检测标志和跨通道风检测标志,每个风检测标志指示风噪声的存在或不存在；以及融合平滑模块,其用于接收多个风检测标志并生成输出风检测标志。麦克风生成多个音频输入信号。风检测器和融合平滑模块可以包括存储在存储器中以供由数字信号处理器执行的程序指令。风检测器是：单通道检测器,其用于接收音频输入信号的单音频通道并生成单通道风噪声标志；以及跨通道检测器,其用于计算两个或更多个音频通道之间的自相关和互相关。(The system and method comprises: a wind detector for receiving an audio input signal and outputting wind detection signatures comprising a single channel wind detection signature and a cross-channel wind detection signature, each wind detection signature indicating the presence or absence of wind noise; and a fusion smoothing module for receiving the plurality of wind detection signatures and generating an output wind detection signature. The microphone generates a plurality of audio input signals. The wind detector and fusion smoothing module may include program instructions stored in memory for execution by the digital signal processor. The wind detector is: a single channel detector for receiving a single audio channel of an audio input signal and generating a single channel wind noise signature; and a cross-channel detector for calculating auto-and cross-correlations between two or more audio channels.)

1. A system, comprising:

a wind detector operable to receive a plurality of audio input signals and to output a plurality of wind detection flags, the plurality of wind detection flags comprising a single channel wind detection flag and a cross-channel wind detection flag, each wind detection flag indicating the presence or absence of wind noise; and

a fusion smoothing module operable to receive the plurality of wind detection signatures and generate an output wind detection signature, the output wind detection signature.

2. The system of claim 1, further comprising a plurality of microphones operable to sense sound and generate the plurality of audio input signals.

3. The system of claim 1, further comprising a memory storing program instructions, and a digital signal processor operable to execute the program instructions; and wherein the wind detector and the fusion smoothing module comprise program instructions stored in the memory.

4. The system of claim 1, further comprising a noise suppression module operable to receive the audio input signal and the output wind detection flag and reduce wind noise detected in the audio input signal.

5. The system of claim 1, further comprising an active noise cancellation system operable to generate an anti-noise signal to cancel portions of the audio input signal as a function of the output wind detection signature.

6. The system of claim 1, wherein the wind detector comprises a single channel detector operable to receive a single audio channel of the plurality of audio input signals and to generate the single channel wind detection flag.

7. The system of claim 6, wherein the single channel detector is operable to compare the single audio channel to a wind spectral model.

8. The system of claim 7, wherein the wind spectrum model includes a mean and a standard deviation of power ratios and a spectral slope for portions of frequency components, and wherein wind noise is determined to be absent if the mean of the power ratios is less than a threshold mean or the standard deviation of the power ratios is greater than a threshold standard deviation; and wherein wind is determined to be present if the spectral slope is greater than a predetermined threshold spectral slope.

9. The system of claim 6, wherein the wind detector comprises a cross-channel detector operable to calculate an autocorrelation and a cross-correlation between two or more audio channels, and wherein the presence of wind is determined if the autocorrelation is less than the cross-correlation.

10. The system of claim 1, wherein the fusion smoothing module is operable to set the output wind detection flag to present if the cross-channel wind detection flag is on and at least one mono wind detection flag is on.

11. The system of claim 1, wherein the blending smoothing function is operable to set the blended wind flag if a predetermined number of previously generated blended wind flags are on.

12. A method, comprising:

receiving a plurality of audio input signals;

generating a plurality of preliminary wind detection flags, the plurality of preliminary wind detection flags including a single-channel wind detection flag and a cross-channel wind detection flag, each wind detection flag indicating a presence or absence of wind noise in a portion of the audio input signal; and

outputting the wind detection flag.

13. The method of claim 12, further comprising reducing wind noise in the audio input signal if the wind detection flag is active.

14. The method of claim 12, further comprising generating an anti-noise signal to cancel portions of the audio input signal according to the wind detection flag.

15. The method of claim 12, further comprising receiving a single audio channel of the audio input signal and generating the single channel wind detection flag.

16. The method of claim 15, further comprising comparing the single audio channel to a wind spectral model.

17. The method of claim 16, further comprising generating the wind spectral model by calculating a mean and a standard deviation of power ratios of specific frequency components and a spectral slope;

setting the single channel wind detection flag to indicate absence of wind noise if the mean of the power ratio is less than a threshold mean or the standard deviation is greater than a threshold standard deviation; and

setting the single channel wind noise flag to indicate the presence of wind noise if the spectral slope is greater than a predetermined threshold spectral slope.

18. The method of claim 16, further comprising calculating auto-and cross-correlations between two or more audio channels; and determining that wind noise is present if the autocorrelation is less than the cross-correlation.

19. The method of claim 12, further comprising setting a final wind detect flag to present if the cross-channel detector wind noise flag is on and at least one of the single channel audio flags is on.

20. The method of claim 19, further comprising smoothing the fused wind detection flag based on a previously determined number of fused wind detection flag values.

Technical Field

The present application relates generally to noise cancellation systems and methods, and more particularly, for example, to the cancellation and/or suppression of wind noise in audio processing devices such as earphones, e.g., over-the-ear, and in-ear), earplugs, and hearing aids, as well as other personal listening devices.

Background

Audio processing devices typically include one or more microphones to sense sounds from the environment and generate corresponding audio signals. For example, an Active Noise Cancellation (ANC) headset includes a reference microphone to generate an anti-noise signal that is approximately equal in magnitude to the sensed ambient noise, but opposite in phase. The ambient noise and the anti-noise signal acoustically cancel each other, thereby allowing the user to hear the desired audio signal.

However, conventional ANC systems (and other noise reduction or noise cancellation systems) do not completely cancel all of the noise, leaving residual noise and/or generating audible artifacts (artifacts) that may distract the user. For example, unlike ambient sound that is eliminated in an ANC system, wind noise may appear at the microphone in response to local air turbulence at the microphone components. Wind noise may be independent of ambient noise reaching the ear canal of the user, and the corresponding anti-noise signal may be audible to the user. Noise suppression systems that attempt to remove background noise from audio signals face similar challenges in removing wind noise.

In view of the foregoing, there is a continuing need for improved noise reduction and noise cancellation systems and methods for audio signals that may include sensed wind noise. There is also a continuing need for improved active noise cancellation systems and methods for headphones, earpieces, and other personal listening devices that may operate in windy environments.

Disclosure of Invention

Improved systems and methods for active noise cancellation and/or noise suppression in audio devices that may be used in windy environments are disclosed herein. In one or more embodiments, a system comprises: a wind detector operable to receive a plurality of audio input signals and output a plurality of wind detection flags, the plurality of wind detection flags comprising a single channel wind detection flag and a cross-channel wind detection flag, each wind detection flag indicating the presence or absence of wind noise; and a fusion smoothing module operable to receive the plurality of wind detection signatures and generate an output wind detection signature, the output wind detection signature.

The system may also include a plurality of microphones operable to sense sound and generate a plurality of audio input signals, and a memory storing program instructions, and a digital signal processor operable to execute the program instructions. In various embodiments, the system may include: a noise suppression module operable to receive the audio input signal and output a wind detection flag and reduce wind noise detected in the audio input signal; and/or an active noise cancellation system operable to generate an anti-noise signal to cancel portions of the audio input signal in accordance with the output wind detection signature.

In various embodiments, the wind detector includes a single channel detector operable to receive a single audio channel of the plurality of audio input signals and generate a single channel wind detection signature. The single channel detector can be operable to compare the single audio channel to a wind spectral model that includes a mean and standard deviation of power ratios and a spectral slope of portions of the frequency components. The wind detector is operable to: clearing the flag if the mean of the power ratios is less than the threshold mean and the standard deviation is greater than the threshold standard deviation (e.g., when it is determined that wind noise is not present); and setting a flag if the spectral slope is greater than a predetermined threshold spectral slope (e.g., when wind noise is determined to be present). The wind detector may also include a cross-channel detector operable to calculate an autocorrelation and a cross-correlation between two or more audio channels, and set a flag if the autocorrelation is less than the cross-correlation.

The fusion smoothing module may be operable to set the output wind detection flag to "present" if the cross-channel wind detection flag is on and the at least one mono-channel wind detection flag is on, and to set the fusion wind flag if a predetermined number of previously generated fusion wind flags are on.

In one or more embodiments, a method includes receiving a plurality of audio input signals, generating a plurality of preliminary wind detection flags, the plurality of preliminary wind detection flags including a single-channel wind detection flag and a cross-channel wind detection flag, each wind detection flag indicating a presence or absence of wind noise in a portion of the audio input signals, and outputting the wind detection flags. The method may further comprise: reducing wind noise in the audio input signal if the wind detection flag is active (active), and/or generating an anti-noise signal to cancel a portion of the audio input signal in dependence on the wind detection flag.

In various embodiments, the method includes receiving a single audio channel of an audio input signal and generating a single channel wind detection signature, generating a wind spectral model by calculating a mean and a standard deviation of power ratios for particular frequency components and a spectral slope, and comparing the single audio channel to the wind spectral model. If the mean of the power ratios is less than the threshold mean and the standard deviation is greater than the threshold standard deviation, the method may set a single channel wind detection flag to indicate the absence of wind noise. If the spectral slope is greater than the predetermined threshold spectral slope, the method may set a single-channel wind noise flag to indicate the presence of wind noise.

The method may further include calculating an autocorrelation and a cross-correlation between two or more audio channels, and determining that wind noise is present if the autocorrelation is less than the cross-correlation. The final wind detect flag may be set to "present" if the cross-channel detector wind noise flag is on and at least one of the single channel audio flags is on. The method may further smooth the fused wind detection flag based on a previously determined number of fused wind detection flag values.

The scope of the invention is defined by the claims, which are incorporated into this section by reference. A more complete understanding of embodiments of the present disclosure will be afforded to those skilled in the art, as well as a realization of additional advantages thereof, by a consideration of the following detailed description of one or more embodiments. Reference will be made to the appended sheets of drawings which will first be described briefly.

Drawings

Aspects of the present disclosure and its advantages are better understood by referring to the following drawings and detailed description. It should be understood that like reference numerals are used to identify like elements illustrated in one or more of the figures, which are presented for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting the embodiments of the present disclosure. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure.

Fig. 1 illustrates a wind detection system in accordance with one or more embodiments of the present disclosure.

FIG. 2 illustrates a flow diagram of a single channel wind detector in accordance with one or more embodiments of the present disclosure.

FIG. 3 illustrates a flow diagram of a cross-channel wind detector in accordance with one or more embodiments of the present disclosure.

Fig. 4 illustrates a flow diagram of a fusion phase in a fusion smoothing module in accordance with one or more embodiments of the present disclosure.

FIG. 5 illustrates a flow diagram of the smoothing phase in the fusion smoothing module.

Fig. 6 illustrates an embodiment of an audio device in accordance with one or more embodiments of the present disclosure.

Detailed Description

Improved wind noise detection systems and methods are disclosed that may be implemented in various audio processing systems including Active Noise Cancellation (ANC) systems, mobile phones, smart speakers, voice command and processing systems, automotive systems (e.g., hands-free voice control), and other audio processing systems that may operate in windy environments.

In one embodiment, the wind noise detection system comprises two or more spatially separated microphones. Each microphone senses sound in the environment, which may include wind noise sensed due to air turbulence local to each microphone. Thus, different microphones may independently sense different wind noise events. The wind noise detection system analyzes a single-channel wind signature associated with each microphone and cross-channel wind signatures of two or more audio channels. In one embodiment, the single channel wind signature characterizes the spectrum of the wind noise, while the cross-correlation between the microphone signal pairs is evaluated across the channel wind signatures. The fusion smoothing phase works to fuse result features, filter detection results, and improve system stability.

The systems and methods disclosed herein provide a number of advantages over conventional solutions. For example, the wind detection systems and methods of the present disclosure explore both single-channel wind features and cross-channel wind features and employ a fused smoothing phase to filter the detection results. For example, the single channel feature detector may include a unique decision tree structure as disclosed herein, and the features may include the mean and standard deviation of the low frequency component power ratio and the spectral slope between 500 Hz and 1000 Hz. The calculated ratio mean and standard deviation provide a good indicator of separating wind noise from speech for use in various speech applications. Furthermore, the spectral slope allows to distinguish between wind noise and ambient background noise (such as office and street noise). The cross-channel feature provides cross-correlation of the two channel signals. Unlike methods that work in the time domain, the proposed wind detection system can calculate the cross-correlation in the frequency domain. In various embodiments, for example, the phase information may be discarded and/or the cross-correlation may be performed for the entire frequency band or using only low frequency components.

Referring to fig. 1, a wind detection system 100 in accordance with one or more embodiments will now be described. The wind detection system 100 may implement an audio processing system having two or more microphones to monitor the environment for the presence of a wind. In some embodiments, the wind detection system 100 is implemented in an audio input processing component of an audio device that may include a Digital Signal Processor (DSP) configured to suppress noise, detect speech, separate a target signal, and/or perform other multi-channel audio input processing. The wind detection system 100 may generate wind noise information that may be used to optimize noise suppression performance of the audio device. For example, a two-channel wind detection system may be used on headphones equipped with two external microphones (e.g., on the left and right sides) to monitor ambient sounds.

The wind detection system 100 includes a plurality of microphones or other audio sensors, such as left and right microphones 102 and 104, a wind detector module 110, and a fusion smoothing module 140. Each microphone (102 and 104) senses sound in the external environment, which may include sound from a desired target source 106, sound from a noise source, and sound generated locally by the wind. Each microphone generates an input audio signal that is digitally sampled and converted to the frequency domain as a left channelAnd a right channelWhere f is the frequency.

Wind detector module 110 receivesAndas an input. The wind detector module 110 includes a plurality of detector sub-modules configured to analyze characteristics of the input signal. In the illustrated embodiment, the wind detector module 110 includes a single left channel detector 112, a single right channel detector 116, and a cross-channel detector 114. The wind detector system 100 may include additional microphones and the wind detector module 110 may include additional single-channel detectors corresponding to each microphone and additional cross-channel detectors corresponding to a grouping of two of the plurality of microphones.

The single left channel detector 112 willAnd comparing with a wind spectrum model. Features to be considered in the comparison may include: (1) low frequency componentAndthe mean and standard deviation of the power ratio of (a); and (2) spectral slope. The low frequency component will now be describedAndthe mean and standard deviation of the power ratio of (a). It has been observed that wind noise is generally concentrated in low frequency bands (e.g.,<1000 Hz), while human speech (e.g., the desired target audio in a speech-controlled device) has a higher high frequency power and its power distribution is time-dependent. Thus, the power ratio of low frequency components in wind noise is less correlated and more stable over time than speech signals. In one embodiment, the power ratio is calculated by

Wherein f is_thIs a low frequency threshold. The single left channel detector 112 will average and standard deviation、Andand its threshold valueAnda comparison is made. If it is notOrIt indicates that there is no wind noise present and that speech is dominant in the signal.

The spectral slope will now be described with reference to fig. 2And (4) calculating. It has been observed that wind noise typically has a linear spectral slope between 500 Hz and 1000 Hz. The single left channel detector 112 willAnd expected slope thresholdA comparison is made. If it is notIt indicates that there is no wind present and that background noise is dominant in the signal. Fig. 2 illustrates an embodiment of a process 200 for operating the single left channel detector 112. Process 200 may be implemented in various combinations of hardware and software, including, for example, program instructions as stored in a memory for execution by a digital processor.

In step 202, the single left channel detector calculates the total signal power. In step 204, the single left channel detector calculates the power of the low frequency component as. Next, in step 206, a power ratio is calculated And in step 208, the mean and standard deviation are updated、And. In step 210, ifOrThen the current signal is speech and there is no wind. The detector then clears the wind flag and provides the wind flag as output in step 218. If it is notOrIf false, the single left channel detector calculates the spectral slope between 500 Hz and 1000 Hz in step 212. In step 214, ifThen the current signal is background noise and there is no wind. The detector clears 218 and outputs the air flag. If it is notFalse, the current signal is neither speech nor background noise. In step 216 it is determined that wind is present and a wind flag is set and output.

The process 200 may also be used to detect the presence or absence of noise by the single right channel detector 116. Single right channel detector 116 may store program instructions for causing a processor to perform process 200, which process 200 applies toTo set the wind flag for the right input audio channel.

In various embodiments, a two-stage decision checking process is used to distinguish wind noise from speech and background noise. The process includes treatingAndboth cross-channel detectors 114. In some embodiments, the cross-channel detector 114 is implemented as program instructions stored in a memory for instructing a digital signal processor to perform the processes disclosed herein. In one embodiment, the cross-channel detector 114 is configured to calculate the auto-and cross-correlations of the left and right channels as follows:

note that the correlation parameters were calculated in the above example without phase information、And. Wind noise may be created by local air turbulence at each microphone, which results in a difference between the wind signals observed at the left and right microphones. The cross-channel detector is toAnda comparison is made whereinIs a threshold coefficient. If it is notThen it is determined that wind is present and a wind flag is set.

FIG. 3 illustrates an embodiment of a process 300 for operating the cross-channel detector 114. In step 302, the cross-channel detector calculates the autocorrelation And. In step 304, a cross-correlation is calculated. In step 306, ifThen in step 308 it is determined that wind is present and a wind flag is set across the channel detector 114. Otherwise, in step 310, cross-channel detector 114 clears the wind flag.

Referring back to FIG. 1, the wind detector module 110 outputs the results of the single left channel detector 112, the single right channel detector 116, and the cross-channel detector 114 to the fusion smoothing module 140. The results of each of the three detectors are fused by rules that determine wind detection. For example, when the wind flag of at least one of the cross-channel detector 114 and the single-channel detectors 112 and 116 is set, the output of each of the three detectors may be fused by the rule of determining the presence of wind.

Referring to fig. 4, an embodiment of the operation of the fusion module 142 will now be described. Process 400 may be implemented in various hardware and/or software configurations including, for example, program instructions as stored in a memory of an audio processor for execution by a digital signal processor. If the wind flag across the channel detector 114 is off in step 402, then there is no wind, then the fusion wind flag is cleared in step 410. In step 404, if the cross-channel detector 114 wind flag is on and the single left channel detector 112 flag is on, then in step 408 it is determined that wind is present and the fusion wind flag is set. In step 406, if the single left channel detector flag is not on and the wind flag of the single right channel detector 116 is on, then in step 408 it is determined that wind is present and the blended wind flag is set. Otherwise, there is no wind and the fusion wind flag is cleared in step 410.

The fusion wind flag is further smoothed to account for missed detections and false alarm events. For example, in one embodiment, the smoothing method checks the last N fused wind flags to determine whether to change the wind detection state. If all of the last N wind flags are on, then the smoothed wind flag is on. If all of the last N wind flags are off, then the smoothed wind flag is off. Otherwise, the smoothed wind flag may remain in its current state. Other settings and algorithms may also be used to increase or decrease the sensitivity to wind detection events, depending on the goals of the system.

Referring to fig. 5, an embodiment of the smoothing operation performed by the smoothing module 144 of fig. 1 will now be described. The smoothing operation 500 may be implemented in various hardware and software configurations including, for example, program instructions as stored in a memory for execution by a digital signal processor. In step 502, if the smoothed wind flag is "on" and the fusion phase wind flag is "on" (step 504), the fusion flag counter is reset in step 506. If the fusion phase wind flag is "off," the fusion flag counter is incremented by one in step 508. Referring back to step 502, if the smoothed wind flag is "off" and the fusion phase wind flag is "off" (step 510), the fusion flag counter is reset in step 512. If the fusion phase wind flag is "on" (step 510), the fusion flag counter is incremented by one in step 508. In step 514, if the blend flag counter is greater than or equal to N (e.g., where N is equal to the number of consecutive wind flags), the smoothed wind flag is set equal to the blend wind flag (step 516).

In various embodiments, wind detection may be implemented in various devices having two or more microphones, such as cell phones, PDAs, smart speakers, smart watches, headsets, and hearing aids. There are many frequency domain transformation algorithms for microphone signals, such as fourier transforms and wavelet transforms. The present disclosure is not limited to one particular algorithm. The proposed wind detector can be extended to a multiple microphone case. The wind detector module can output、、Andrather than the detector wind signature. The wind detector module may smooth features、、Andto obtain long-term feature estimates prior to threshold comparison. The features may be smoothed by FIR filters and IIR filters. The fusion smoothing module can employ other common machine learning algorithms to fuse wind detector module results, such as logistic regression, naive bayes, and neural networks. The fusion smoothing module may employ other commonly used filtering algorithms to perform result smoothing, such as median filtering, FIR filtering, and IIR filtering.

With reference to fig. 6, an example system incorporating the wind detection process of the present disclosure will now be described. The audio device 600 includes audio inputs, such as an audio sensor array 605, an audio signal processor 620, and a host system component 650. Audio sensor array 605 includes one or more sensors, each of which can convert sound waves into an audio signal. In the illustrated environment, the audio sensor array 605 includes a plurality of microphones 605a-605n, each of which generates one audio channel of a multi-channel audio signal.

The audio signal processor 620 includes an audio input circuit 622, a digital signal processor 624, and an optional audio output circuit 626. In various embodiments, the audio signal processor 620 may be implemented as an integrated circuit comprising analog circuitry, digital circuitry, and a digital signal processor 624 operable to execute program instructions stored in a memory. For example, the audio input circuit 622 may include an interface to the audio sensor array 605, anti-aliasing filters, analog-to-digital converter circuitry, echo cancellation circuitry, and other audio processing circuitry and components.

The digital signal processor 624 may include one or more of a processor, a microprocessor, a single-core processor, a multi-core processor, a microcontroller, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)), a Digital Signal Processing (DSP) device, or other logic devices that may be configured by hardwiring, executing software instructions, or a combination of both to perform the various operations discussed herein with respect to embodiments of the disclosure.

The digital signal processor 624 is operable to process the multi-channel digital audio input signal to generate an enhanced audio signal that is output to one or more host system components 650. The digital signal processor 624 is operable to interface with and communicate with a host system component 650, such as through a bus or other electronic communication interface. In various embodiments, the multi-channel audio signal includes a mixture of a noise signal and at least one desired target audio signal (e.g., human voice), and the digital signal processor 624 is operable to isolate or enhance the desired target signal while reducing or eliminating undesired noise signals. The digital signal processor 624 can be operable to perform wind noise detection, voice/keyword detection and processing, echo cancellation, noise cancellation, target signal tracking and enhancement, post-filtering, and other audio signal processing.

In the illustrated embodiment, the digital signal processor 624 includes a wind detector 628 (e.g., the wind detector module 110 of fig. 1) and a fusion smoothing component 630 (e.g., the fusion smoothing module 140 of fig. 1) operable to determine whether wind noise is present in the current audio sample. The audio signal processor 620 may be configured to generate an enhanced target signal for further processing by host system components (e.g., voice input for voice communications, voice command processing, etc.). The digital signal processor 624 may use the indication of the presence or absence of wind noise in a noise suppression process to help remove detected wind noise. In another embodiment, the audio signal processor 620 may be configured for active noise cancellation, and the indication of the presence or absence of wind noise may be used by the digital signal processor 624 to assist in the generation of the anti-noise signal. The digital signal processor 624 may also include other modules that utilize the final wind detection flag, such as a noise suppression/cancellation module 632. In various embodiments, the noise suppression/cancellation module 632 may provide noise suppression of wind noise in the input audio signal and/or generate anti-noise for active noise cancellation under windy conditions.

The audio output circuit 626 processes the audio signal received from the digital signal processor 624 for output to at least one speaker, such as speakers 610a and 610 b. The audio output circuit 626 may include a digital-to-analog converter that converts one or more digital audio signals to corresponding analog signals and one or more amplifiers for driving the speakers 610a and 610 b.

The audio device 600 may be implemented as any device operable to receive and detect target audio data, such as, for example, a mobile phone, a smart speaker, a tablet computer, a laptop computer, a desktop computer, a voice-controlled appliance, or an automobile. The host system components 650 may include various hardware and software components for operating the audio device 600. In the illustrated embodiment, the host system component 650 includes a processor 652, a user interface component 654, a communication interface 656 for communicating with external devices and networks, such as a network 680 (e.g., the internet, cloud, local area network, or cellular network) and a mobile device 684, and a memory 658.

The processor 652 may include one or more of a processor, a microprocessor, a single-core processor, a multi-core processor, a microcontroller, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)), a Digital Signal Processing (DSP) device, or other logic devices that may be configured by hardwiring, executing software instructions, or a combination of both to perform the various operations discussed herein with respect to embodiments of the disclosure. The host system component 650 is operable to interface and communicate with the audio signal processor 620 and other host system components 650, such as through a bus or other electronic communication interface.

It will be understood that while the audio signal processor 620 and the host system component 650 are shown as combining hardware components, circuitry, and software, in some embodiments at least some or all of the functionality that the hardware components and circuitry are operable to perform may be implemented as software modules executed by the processor 652 and/or the digital signal processor 624 in response to software instructions and/or configuration data stored in the memory 658 or firmware of the digital signal processor 624.

The memory 658 may be implemented as one or more memory devices operable to store data and information, including audio data and program instructions. The memory 658 may include one or more different types of memory devices, including volatile and non-volatile memory devices, such as RAM (random access memory), ROM (read only memory), EEPROM (electrically erasable read only memory), flash memory, hard drives, and/or other types of memory.

The processor 652 can be operable to execute software instructions stored in the memory 658. In various embodiments, the speech recognition engine 660 is operable to process the enhanced audio signal received from the audio signal processor 620, including recognizing and executing voice commands. The voice communication component 662 can be operable to facilitate voice communication with one or more external devices, such as the mobile device 684 or the user device 686, such as over a voice call over a mobile or cellular telephone network or a VoIP call over an IP (internet protocol) network. In various embodiments, the voice communication includes transmitting the enhanced audio signal to an external communication device.

The user interface components 654 may include a display, a touch-panel display, a keypad, one or more buttons, and/or other input/output components that are operable to enable a user to directly interact with the audio device 600. The communication interface 656 facilitates communication between the audio device 600 and external devices. For example, the communication interface 656 may enable a Wi-Fi (e.g., 802.11) or bluetooth connection between the audio device 600 and one or more local devices, such as the mobile device 684, or a wireless router that provides network access to a remote server 682, such as over the network 680. In various embodiments, the communication interface 656 may include other wired and wireless communication components that facilitate direct or indirect communication between the audio device 600 and one or more other devices.

The foregoing disclosure is not intended to limit the disclosure to the precise forms or particular fields of use disclosed. It is therefore contemplated that various alternative embodiments and/or modifications (whether explicitly described or implied herein) to the present disclosure are possible in light of the present disclosure. Having thus described examples of the present disclosure, persons of ordinary skill in the art will recognize that changes may be made in form and detail without departing from the scope of the disclosure. Accordingly, the disclosure is limited only by the claims.