EP2567377A1 - Composant de suppression/remplacement du vent à utiliser avec des systèmes électroniques - Google Patents

Composant de suppression/remplacement du vent à utiliser avec des systèmes électroniques

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Publication number
EP2567377A1
EP2567377A1 EP11778185A EP11778185A EP2567377A1 EP 2567377 A1 EP2567377 A1 EP 2567377A1 EP 11778185 A EP11778185 A EP 11778185A EP 11778185 A EP11778185 A EP 11778185A EP 2567377 A1 EP2567377 A1 EP 2567377A1
Authority
EP
European Patent Office
Prior art keywords
wind
detector
signal
noise
speech
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP11778185A
Other languages
German (de)
English (en)
Other versions
EP2567377A4 (fr
Inventor
Nicolas Petit
Gregory Burnett
Michael Goertz
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
AliphCom LLC
Original Assignee
AliphCom LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US12/772,963 external-priority patent/US8452023B2/en
Priority claimed from US12/772,975 external-priority patent/US8488803B2/en
Application filed by AliphCom LLC filed Critical AliphCom LLC
Publication of EP2567377A1 publication Critical patent/EP2567377A1/fr
Publication of EP2567377A4 publication Critical patent/EP2567377A4/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R1/00—Details of transducers, loudspeakers or microphones
    • H04R1/08—Mouthpieces; Microphones; Attachments therefor
    • H04R1/083—Special constructions of mouthpieces
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R1/00—Details of transducers, loudspeakers or microphones
    • H04R1/10—Earpieces; Attachments therefor ; Earphones; Monophonic headphones
    • H04R1/1083—Reduction of ambient noise
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R1/00—Details of transducers, loudspeakers or microphones
    • H04R1/46—Special adaptations for use as contact microphones, e.g. on musical instrument, on stethoscope
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R19/00—Electrostatic transducers
    • H04R19/01—Electrostatic transducers characterised by the use of electrets
    • H04R19/016—Electrostatic transducers characterised by the use of electrets for microphones
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones
    • H04R3/005—Circuits for transducers, loudspeakers or microphones for combining the signals of two or more microphones
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R31/00—Apparatus or processes specially adapted for the manufacture of transducers or diaphragms therefor
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02165—Two microphones, one receiving mainly the noise signal and the other one mainly the speech signal
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/78—Detection of presence or absence of voice signals
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/93—Discriminating between voiced and unvoiced parts of speech signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2410/00—Microphones
    • H04R2410/07—Mechanical or electrical reduction of wind noise generated by wind passing a microphone

Definitions

  • the disclosure herein relates generally to noise suppression.
  • this disclosure relates to noise suppression systems, devices, and methods for use in acoustic applications.
  • voiced and unvoiced speech are critical to many speech applications including speech recognition, speaker verification, noise suppression, and many others.
  • speech from a human speaker is captured and transmitted to a receiver in a different location.
  • noise sources that pollute the speech signal, the signal of interest, with unwanted acoustic noise. This makes it difficult or impossible for the receiver, whether human or machine, to understand the user's speech.
  • Typical methods for classifying voiced and unvoiced speech have relied mainly on the acoustic content of single microphone data, which is plagued by problems with noise and the corresponding uncertainties in signal content. This is especially problematic with the proliferation of portable communication devices like mobile telephones.
  • There are methods known in the art for suppressing the noise present in the speech signals but these generally require a robust method of determining when speech is being produced.
  • FIG. 1 is a block diagram of the communications system, under an embodiment.
  • Figure 2 is a block diagram of a wind detector, under an embodiment.
  • Figure 3 is a flow diagram for controlling processing of received signals that include wind noise, under an embodiment.
  • Figure 4 is a low-pass wind detection filter response, under an
  • Figure 5 is the magnitude response of the SSM equalization filter, under an embodiment.
  • Figure 6 is an example look-up table mapping wind index to cutoff frequency, under an embodiment.
  • Figure 7 is a filter response of a low-pass and corresponding high-pass filter used in mixing SSM and microphone audio, under an embodiment.
  • Figure 8 is a magnitude response of a filter used to produce receive wind comfort noise, under an embodiment.
  • Figure 9 is a magnitude response of a filter used to produce transmit wind comfort noise, under an embodiment.
  • Figure 10 is an example plot comparing the speech response of a system with no wind, with 10 mph wind, and with 10 mph wind and wind suppression, under an embodiment.
  • Figure 11 is a two-microphone adaptive noise suppression system, under an embodiment.
  • Figure 12 is an array and speech source (S) configuration, under an embodiment.
  • the microphones are separated by a distance approximately equal to 2d 0 , and the speech source is located a distance d s away from the midpoint of the array at an angle ⁇ .
  • the system is axially symmetric so only d s and ⁇ need be specified.
  • Figure 13 is a block diagram for a first order gradient microphone using two omnidirectional elements Oi and 0 2 , under an embodiment.
  • Figure 14 is a block diagram for a DOMA including two physical microphones configured to form two virtual microphones Vi and V 2 , under an embodiment.
  • Figure 15 is a block diagram for a DOMA including two physical microphones configured to form N virtual microphones v through V N , where N is any number greater than one, under an embodiment.
  • Figure 16 is an example of a headset or head-worn device that includes the DOMA, as described herein, under an embodiment.
  • Figure 17 is a flow diagram for denoising acoustic signals using the DOMA, under an embodiment.
  • Figure 18 is a flow diagram for forming the DOMA, under an
  • Figure 19 is a plot of linear response of virtual microphone V 2 to a 1 kHz speech source at a distance of 0.1 m, under an embodiment.
  • the null is at 0 degrees, where the speech is normally located.
  • Figure 20 is a plot of linear response of virtual microphone V 2 to a 1 kHz noise source at a distance of 1.0 m, under an embodiment. There is no null and all noise sources are detected.
  • Figure 21 is a plot of linear response of virtual microphone i to a 1 kHz speech source at a distance of 0.1 m, under an embodiment. There is no null and the response for speech is greater than that shown in Figure 19.
  • Figure 22 is a plot of linear response of virtual microphone Vi to a 1 kHz noise source at a distance of 1.0 m, under an embodiment. There is no null and the response is very similar to V 2 shown in Figure 20.
  • Figure 23 is a plot of linear response of virtual microphone Vi to a speech source at a distance of 0.1 m for frequencies of 100, 500, 1000, 2000, 3000, and 4000 Hz, under an embodiment.
  • Figure 24 is a plot showing comparison of frequency responses for speech for the array of an embodiment and for a conventional cardioid microphone.
  • Figure 25 is a plot showing speech response for Vi (top, dashed) and V 2 (bottom, solid) versus B with d s assumed to be 0.1 m, under an embodiment.
  • the spatial null in V 2 is relatively broad.
  • Figure 26 is a plot showing a ratio of Vi/V 2 speech responses shown in Figure 10 versus B, under an embodiment.
  • the ratio is above 10 dB for all 0.8 ⁇ B ⁇ 1.1. This means that the physical ⁇ of the system need not be exactly modeled for good performance.
  • Figure 29 is a plot of amplitude (top) and phase (bottom) response of
  • the resulting phase difference clearly affects high frequencies more than low.
  • Non-unity B affects the entire frequency range.
  • the cancellation remains below -10 dB for frequencies below 6 kHz.
  • the cancellation is below -10 dB only for frequencies below about 2.8 kHz and a reduction in performance is expected .
  • the noise has been reduced by about 25 dB and the speech hardly affected, with no noticeable distortion .
  • Figure 34 is a configuration of a two-microphone array with speech source S, under an embodiment.
  • Figure 35 is a block diagram of V 2 construction using a fixed ⁇ ( ⁇ ), under an embodiment.
  • Figure 36 is a block diagram of V 2 construction using an adaptive ⁇ ( ⁇ ), under an embodiment.
  • Figure 37 is a block diagram of Vi construction, u nder an embodiment.
  • Figure 38 is a flow diagram of acoustic voice activity detection, under an embodiment.
  • Figure 39 shows experimental results of the algorithm using a fixed beta when only noise is present, under an embodiment.
  • Figure 40 shows experimental results of the algorithm using a fixed beta when only speech is present, under an embodiment.
  • Figure 41 shows experimental results of the algorithm using a fixed beta when speech and noise is present, under an embodiment.
  • Figure 42 shows experimental results of the algorithm using an adaptive beta when only noise is present, under an embodiment.
  • Figure 43 shows experimental results of the algorithm using an adaptive beta when only speech is present, under an embodiment.
  • Figure 44 shows experimental results of the algorithm using an adaptive beta when speech and noise is present, under an embodiment.
  • Figure 45 is a block diagram of a NAVSAD system, under an embodiment.
  • Figure 46 is a block diagram of a PSAD system, under an embodiment.
  • Figure 47 is a block diagram of a denoising system, referred to herein as the Pathfinder system, under an embodiment.
  • Figure 48 is a flow diagram of a detection algorithm for use in detecting voiced and unvoiced speech, under an embodiment.
  • Figure 49A plots the received GEMS signal for an utterance along with the mean correlation between the GEMS signal and the Mic 1 signal and the threshold for voiced speech detection.
  • Figure 49B plots the received GEMS signal for an utterance along with the standard deviation of the GEMS signal and the threshold for voiced speech detection.
  • Figure 50 plots voiced speech detected from an utterance along with the GEMS signal and the acoustic noise.
  • Figure 51 is a microphone array for use under an embodiment of the PSAD system.
  • Figure 52 is a plot of ⁇ versus di for several Ad values, under an embodiment.
  • Figure 53 shows a plot of the gain parameter as the sum of the absolute values of H-i(z) and the acoustic data or audio from microphone 1.
  • Figure 54 is an alternative plot of acoustic data presented in Figure 53.
  • Figure 55 is a cross section view of an acoustic vibration sensor, under an embodiment.
  • Figure 56A is an exploded view of an acoustic vibration sensor, under the embodiment of Figure 55.
  • Figure 56B is perspective view of an acoustic vibration sensor, under the embodiment of Figure 55.
  • Figure 57 is a schematic diagram of a coupler of an acoustic vibration sensor, under the embodiment of Figure 55.
  • Figure 58 is an exploded view of an acoustic vibration sensor, under an alternative embodiment.
  • Figure 59 shows representative areas of sensitivity on the human head appropriate for placement of the acoustic vibration sensor, under an
  • Figure 60 is a generic headset device that includes an acoustic vibration sensor placed at any of a number of locations, under an embodiment.
  • Figure 61 is a diagram of a manufacturing method for an acoustic vibration sensor, under an embodiment.
  • the communications headset example used is the Jawbone Prime Bluetooth headset, produced by AliphCom in San Francisco, CA.
  • This headset uses two omnidirectional microphones to form two virtual microphones using the system described below (see section "Dual Omnidirectional Microphone Array (DOMA)" below) as well as a third vibration sensor to detect human speech inside the cheek on the face of the user.
  • the cheek location is preferred, any sensor that is capable of detecting vibrations reliably (such as an accelerometer or radiovibration detector (see section "Detecting Voiced and Unvoiced Speech Using Both Acoustic and Nonacoustic Sensors” below) can be used as well.
  • Any italicized text herein generally refers to the name of a variable in an algorithm described herein.
  • ADC represents analog to digital converter.
  • AEC represents acoustic echo cancellation
  • DAC represents digital to analog converter
  • EQ represents equalization, generally in terms of frequency.
  • Microphone is a physical acoustic sensing element.
  • Normalized Least Mean Square (NLMS) adaptive filter is a common adaptive filter used to determine correlation between the microphone signals. Any similar adaptive filter may be used.
  • the term Oi represents the first physical omnidirectional microphone
  • the term 0 2 represents the second physical omnidirectional microphone
  • SSM Skin Surface Microphone
  • VAD Voice Activity Detection
  • VAD Voice Activity Detection
  • Virtual microphone is a microphone signal comprised of combinations of physical microphone signals.
  • Wind is the movement of air.
  • Wind comfort noise is wind or wind-like noise that is included in either the transmitted or received signal to alert the user and the person to whom they are speaking to the presence of wind without unduly affecting the communication intelligibility. Wind noise is unwanted acoustic disturbances from air pressure and/or air flow in the microphone signal of interest.
  • FIG. 1 is a block diagram of the communications system, under an embodiment.
  • the wind detection algorithm takes advantage of the fact that the wind noises in each of the microphones are uncorrelated. Indeed, since wind physically displaces air molecules when it flows, it independently moves the diaphragm of each microphone in a non-correlated fashion. Even acoustic wind noise (caused by turbulence near the microphone) is not highly correlated. Thus, the effect on the microphone from wind is chaotic and non-linear. It is true that the intensities of wind in each microphone are slightly correlated, but their waveforms cannot be easily represented by linear transfer functions even if the microphones are only a few millimeters apart.
  • FIG. 2 is a block diagram of a wind detector, under an embodiment.
  • the system of an embodiment includes a first detector that receives a first signal and a second detector that receives a second signal.
  • a voice activity detector (VAD) is coupled to the first detector.
  • the VAD generates a VAD signal when the first signal corresponds to voiced speech.
  • the system includes a wind detector coupled to the second detector.
  • the wind detector correlates signals received at the second detector and derives or generates from the correlation wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector.
  • the wind metrics are used as control signals as described in detail herein. For example, the wind detector controls a
  • the wind detector comprises an adaptive filter coupled to the second detector.
  • the wind detector correlates signals by calculating energy of an adaptive filter error of the adaptive filter.
  • the error is large when the signals are uncorrelated, which is the case for wind noise. Normal acoustic speech and noise are highly correlated between the microphones, which are typically 10-40 mm apart from one another.
  • the wind detector of an embodiment comprises a first exponential averaging filter and a second exponential averaging filter coupled to the adaptive filter.
  • the wind detector applies the energy to the first exponential averaging filter and the second exponential averaging filter.
  • the system of an embodiment comprises a gain controller coupled to the first detector and the wind detector.
  • Figure 3 is a flow diagram for controlling processing of received signals that include wind noise 300, under an embodiment.
  • the signal processing receives a first signal at a first detector and a second signal at a second detector 302.
  • a correlation is determined between signals received at the second detector, and wind metrics are derived from the correlation that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector 304.
  • An embodiment controls configuration of the second detector according to the wind metrics 306.
  • An embodiment generates an output signal for transmission by dynamically mixing the first signal and the second signal according to the wind metrics 308.
  • Figure 4 is a low-pass wind detection filter response, under an embodiment. This region is normally dominated by wind (and not acoustic) noise, making detection more accurate and robust.
  • the filtered signals are then decimated by a factor of 21 to limit the LMS computational workload and provide faster adaptation with fewer adaptive taps on the "whiter" decimated signals.
  • the reference LMS signal in this case low-passed and decimated Oi
  • a delay of 2 (decimated) samples and 7 adaptive LMS taps are used.
  • the energy of the LMS residual is then calculated. It is sent to two smoothing exponential averaging filters Ei(z) and E 2 (z):
  • a first output variable windlndex of this module is obtained by
  • windPresent (binary) is obtained by comparing instantWindLevel to a windPresentThreshold (e.g., -74 dBFS) constant threshold yielding a binary variable equal to 1 when the variable exceeds the threshold. The binary variable is then followed by a hold block that maintains a binary output of 1 for 20 msec whenever the input is 1. This variable is used by other components of the system to suspend activities that would otherwise be negatively affected in the presence of wind.
  • a windPresentThreshold e.g., -74 dBFS
  • a final binary variable windMode is produced by comparing
  • instantWindLevel to windHighLevel a constant threshold of -69 dBFS in this enablement over which wind is deemed to have high impact on intelligibility and comfort.
  • the resulting binary output is then filtered by a 2.5 second moving average filter whose output effectively indicates the portion of time that was windy during the last 2.5 seconds.
  • This hysteresis approach prevents windMode from rapidly switching states and is used in lieu of windPresent in scenarios where such fast changes are undesirable.
  • the SSM level and its frequency response should be adjusted to match the transmit audio in the absence of wind as closely as possible.
  • SSM or similar signal captured at the skin is filtered in order to match as closely as possible speech captured by the primary microphone (01) in the absence of wind or noise.
  • this technique cannot be used to extract high- fidelity speech for several reasons.
  • SSM audio has too little speech content beyond 1 kHz, where it is near or under the sensor's noise floor.
  • Another reason is that there is not a unique transfer function mapping SSM to 01 responses for all phonemes; the transform is specific to each phoneme.
  • the SSM response to speech is affected by facial features near the SSM pickup location, specifically the layer of soft tissues separating SSM from cheekbone. As a result, mic-to-SSM response (even for stationary speech production) varies across users.
  • the optimal SSM-microphone speech response transform can only be approximated.
  • it is achieved in two consecutive stages: a one-size-fit-all static equalization filter and an adaptive gain control stage (AGC) to match the RMS of regular speech.
  • AGC adaptive gain control stage
  • Figure 5 is the magnitude response of the SSM equalization filter, under an embodiment. It is implemented as a cascade of 3 biquad IIR filters, but is not so limited. The filter attempts to match the responses up to about 1 kHz where the SSM speech response becomes too small. The region beyond 1kHz is treated as a stop-band and filtered out to avoid amplifying what is mostly acoustic noise and sensor self-noise.
  • the subsequent AGC stage adjusts its gain to match the root mean square (RMS) of the equalized SSM signal to the RMS of the noise-suppressed speech from 0 to 1 kHz.
  • the gain adjustment only occurs when two conditions are met. The first condition is no wind present, as indicated by windPresent. The second condition involves speech activity.
  • a conservative VAD is used indicating speech activity with high level of confidence at the expense of potentially many false negatives.
  • the idea is that the AGC gain should not be adapted when speech is not occurring. Also, short-lived VAD pulses of less than 60ms are rejected from this binary VAD waveform. Furthermore to increase robustness, the AGC gain is limited to a +/- 25dB range.
  • the omnidirectional microphone array used in Jawbone Prime (DOMA) (see section "Dual Omnidirectional Microphone Array (DOMA)” below) provides relatively good noise suppression performance.
  • DOMA Jawbone Prime
  • the wind detector reports sufficient sustained wind
  • the wind detector's windMode variable turns on.
  • the benefit of further reducing the wind in the microphones outweighs the noise reduction advantage offered by the microphone array.
  • the DOMA array is therefore turned off and the noise suppression algorithm bypassed.
  • a simple way to further reduce the wind-to- speech ratio is to add the signals from both omnidirectional microphones together. While the resulting speech content increases by 6dB, the wind RMS is only increased by about 3dB as wind signals in 01 and 02 are uncorrelated.
  • 01 audio is delayed by a fractional delay that accounts for the travel time of speech from 01 to 02 plus any ADC sampling time difference between 02 and 01 audio channels.
  • the resulting signal needs to be scaled by a correction gain factor to match the speech response outside wind mode.
  • a problem with this technique is the absence of any noise suppression.
  • a basic single-microphone noise suppression algorithm such as spectral subtraction is used to attenuate stationary noise in 16 frequency bands evenly distributed across the spectrum (0-4kHz). These algorithms work by selectively attenuating bands where speech-to-noise (and - wind) ratio is lower than 12dB. The maximum attenuation used here is 8dB when the SNR is lower than 3dB.
  • the dynamic mixing component of an embodiment there is an optimal mix of low-passed SSM and high-passed microphone audio (DOMA or Wind Mode Audio) that can be achieved for each level of wind intensity.
  • the mixer adjusts (dynamic) filters' responses to obtain the desired mix.
  • SSM and microphone audio signals have been processed in previous stages, they must be combined in a seamless fashion for varying amount of wind.
  • the technique used here relies on the observation that the microphones' responses to wind drops as the frequency increases.
  • the wind detector windlndex variable provides a rough but reliable metric of the amount of wind at any time from which an estimate of the wind frequency response can be derived.
  • wind frequency response curve Another important characteristic of the wind frequency response curve is that it tends to decrease with frequency at a constant rate through the spectrum and for varying level of winds until the wind response eventually reaches the noise floor. Note however that this is true only when wind is moderate enough such as not to saturate the microphone(s) and/or ADC converter(s).
  • the high-pass equalizer weights G HP used for the microphone audio are obtained by calculating
  • G HP 20 * log 10 (l - 10 20 ) for each band.
  • a 32-tap linear-phase low-pass FIR filter can be stored in memory and retrieved for each of the 31 wind indexes and the corresponding high-pass filter can be derived by subtracting 1 from the central tap.
  • Figure 7 is a filter response of a low-pass and
  • a limited amount of wind noise is added to both receive and transmit audio to increase both near- and far-end users' awareness to wind impact on the conversation with little negative effect on intelligibility and listening comfort.
  • a complementary approach to wind reduction is to get the near-end user to take a pro-active role in limiting wind exposure.
  • Generation of the comfort wind noise of an embodiment begins by subtracting 02 from 01. This reduces much of the non-wind components of the signal. This difference is modulated by a gain that combines two factors.
  • the first factor is a static gain to guarantee an appropriate level of wind feedback in the speaker.
  • the second factor is a gating factor derived from the binary windPresent variable going through a binary pulse-rejection block rejecting positive pulses shorter than 20ms followed by a hold block with a hold duration of 10ms.
  • a filter is applied that limits the amount of low-frequency wind reaching the headset receiver (whose low- frequency response is poor given its small size) and to scale down the higher frequency content of wind that can be responsible for uncomfortable noise in high wind.
  • the resulting signal is designed to sound like a rumble characteristic of wind heard through speakers without overdriving the receiver.
  • Figure 8 is a magnitude response of a filter used to produce receive wind comfort noise, under an embodiment. Note that this filter was designed based on the specific characteristics of the microphones and speaker used in Jawbone Prime and there may be some changes required for different implementations. The important part is to add enough wind noise to be audible to the headset user but not enough to disrupt the conversation.
  • transmit comfort wind noise is added to the transmitted audio to alert the far-end user that wind is present, providing an explanation for the difference in speech response due to SSM mixing and/or degradation of noise suppression performance.
  • Bluetooth due to differences in Bluetooth
  • a first change is the use of a different static gain to ensure appropriate level of wind feedback on the other end of the line; this gain is set experimentally.
  • Another change is the use of a different filter, where Figure 9 is a magnitude response of a filter used to produce transmit wind comfort noise, under an embodiment.
  • the resulting signal is delayed so as to be
  • Figure 10 shows a plot of a male English speaker speaking in silence (left), in a moderate wind (10 mph, center), and in the same wind with the wind suppression algorithm active (right), under an embodiment.
  • the top is the time series, the middle the spectrogram, and the bottom the energy vs. time.
  • the wind suppression algorithm significantly reduces the wind noise and restores the speech quality and intelligibility.
  • the embodiments described herein take advantage of the vibration sensor's wind immunity and acoustic noise resistance to not only remove wind noise, but restore a significant amount of speech presence and intelligibility.
  • the method of wind suppression involves an adaptive, filtered combination of vibration sensor signal, combined omnidirectional signal, and normal virtual microphone noise suppressed signal. This allows for achievement of significant wind reduction with limited speech distortion despite the severe impact of wind on the microphone signals.
  • a dual omnidirectional microphone array that provides improved noise suppression is described herein.
  • the array of an embodiment is used to form two distinct virtual directional microphones which are configured to have very similar noise responses and very dissimilar speech responses.
  • the only null formed by the DOMA is one used to remove the speech of the user from V 2 .
  • the two virtual microphones of an embodiment can be paired with an adaptive filter algorithm and/or VAD algorithm to significantly reduce the noise without distorting the speech, significantly improving the SNR of the desired speech over conventional noise suppression systems.
  • the embodiments described herein are stable in operation, flexible with respect to virtual microphone pattern choice, and have proven to be robust with respect to speech source-to-array distance and orientation as well as temperature and calibration techniques.
  • bleedthrough means the undesired presence of noise during speech.
  • the term "denoising” means removing unwanted noise from Micl, and also refers to the amount of reduction of noise energy in a signal in decibels (dB).
  • devoicing means removing/distorting the desired speech from
  • directional microphone means a physical directional microphone that is vented on both sides of the sensing diaphragm.
  • Micl (Ml) means a general designation for an adaptive noise suppression system microphone that usually contains more speech than noise.
  • M2 means a general designation for an adaptive noise suppression system microphone that usually contains more noise than speech.
  • noise means unwanted environmental acoustic noise.
  • nucle means a zero or minima in the spatial response of a physical or virtual directional microphone.
  • the term "Oi" means a first physical omnidirectional microphone used to form a microphone array.
  • 0 2 means a second physical omnidirectional microphone used to form a microphone array.
  • speech means desired speech of the user.
  • SSM Skin Surface Microphone
  • VY' means the virtual directional "speech" microphone, which has no nulls.
  • V 2 means the virtual directional "noise” microphone, which has a null for the user's speech.
  • VAD Voice Activity Detection
  • VM virtual microphones
  • VM directional microphones means a microphone constructed using two or more omnidirectional
  • Figure 11 is a two-microphone adaptive noise suppression system 1100, under an embodiment.
  • the two-microphone system 1100 including the combination of physical microphones MIC 1 and MIC 2 along with the
  • the dual omnidirectional microphone array (DOMA) 1110 in analyzing the single noise source 1101 and the direct path to the microphones, the total acoustic information coming into MIC 1 (1102, which can be an physical or virtual microphone) is denoted by mi(n).
  • the total acoustic information coming into MIC 2 (1103, which can also be an physical or virtual microphone) is similarly labeled m 2 (n).
  • Equation 1 This is the general case for all two microphone systems. Equation 1 has four unknowns and only two known relationships and therefore cannot be solved explicitly.
  • Equation 1 Equation 1 reduces to
  • the function Hi(z) can be calculated using any of the available system
  • H 2 (z) N(z) ⁇ 0.
  • M ls (z) which is the inverse of the Hi(z) calculation.
  • H 2 (z) the values calculated for Hi(z) are held constant (and vice versa) and it is assumed that the noise level is not high enough to cause errors in the H 2 (z) calculation.
  • Equation 1 is rewritten as
  • Equation 4 is much simpler to implement and is very stable, assuming Hi(z) is stable. However, if significant speech energy is in M 2 (z), devoicing can occur. In order to construct a well-performing system and use Equation 4, consideration is given to the following conditions: l. Availability of a perfect (or at least very good) VAD in noisy conditions
  • Condition Rl is easy to satisfy if the SNR of the desired speech to the unwanted noise is high enough. "Enough” means different things depending on the method of VAD generation. If a VAD vibration sensor is used, as in Burnett 7,256,048, accurate VAD in very low SNRs (-10 dB or less) is possible.
  • Condition R5 is normally simple to satisfy because for most applications the microphones will not change position with respect to the user's mouth very often or rapidly. In those applications where it may happen (such as hands- free conferencing systems) it can be satisfied by configuring Mic2 so that H 2 (z) » 0 .
  • the DOMA in various embodiments, can be used with the Pathfinder system as the adaptive filter system or noise removal.
  • the Pathfinder system available from AliphCom, San Francisco, CA, is described in detail in other patents and patent applications referenced herein.
  • any adaptive filter or noise removal algorithm can be used with the DOMA in one or more various alternative embodiments or configurations.
  • the Pathfinder system When the DOMA is used with the Pathfinder system, the Pathfinder system generally provides adaptive noise cancellation by combining the two microphone signals (e.g ., Micl, Mic2) by filtering and summing in the time domain .
  • the adaptive filter generally uses the signal received from a first microphone of the DOMA to remove noise from the speech received from at least one other microphone of the DOMA, which relies on a slowly varying linear transfer function between the two microphones for sources of noise.
  • an output signal is generated in which the noise content is attenuated with respect to the speech content, as described in detail below.
  • Figure 12 is a generalized two-microphone array (DOMA) including an array 1201/1202 and speech source S configuration, under an embodiment.
  • Figure 13 is a system 1300 for generating or producing a first order gradient microphone V using two omnidirectional elements Oi and 0 2 , under an embodiment.
  • the array of an embodiment includes two physical microphones 1201 and 1202 (e.g. , omnidirectional microphones) placed a distance 2d 0 apart and a speech source 1200 is located a distance d s away at an angle of ⁇ . This array is axially symmetric (at least in free space), so no other angle is needed.
  • the output from each microphone 1201 and 1202 can be delayed (z x and z 2 ), multiplied by a gain (Ai and A 2 ), and then summed with the other as
  • the output of the array is or forms at least one virtual microphone, as described in detail below. This operation can be over any frequency range desired .
  • VMs virtual microphones
  • a wide variety of virtual microphones (VMs) also referred to herein as virtual directional microphones, can be realized .
  • VMs virtual microphones
  • Figure 14 is a block diagram for a DOMA 1400 including two physical microphones configured to form two virtual microphones Vi and V 2 , under an embodiment.
  • the DOMA includes two first order gradient microphones i and V 2 formed using the outputs of two microphones or elements Oi and 0 2 (1201 and 1202), under an embodiment.
  • the DOMA of an embodiment includes two physical microphones 1201 and 1202 that are omnidirectional microphones, as described above with reference to Figures 12 and 13. The output from each microphone is coupled to a processing
  • the processing component 1402 or circuitry, and the processing component outputs signals representing or corresponding to the virtual microphones V x and V 2 .
  • the output of physical microphone 1201 is coupled to processing component 1402 that includes a first processing path that includes application of a first delay z n and a first gain An and a second processing path that includes application of a second delay z 12 and a second gain A i2 .
  • the output of physical microphone 1202 is coupled to a third processing path of the processing component 1402 that includes application of a third delay z 2 i and a third gain A 2i and a fourth processing path that includes application of a fourth delay z 22 and a fourth gain A 22 .
  • the output of the first and third processing paths is summed to form virtual microphone and the output of the second and fourth processing paths is summed to form virtual microphone V 2 .
  • FIG. 15 is a block diagram for a DOMA 1500 including two physical microphones configured to form N virtual microphones Vi through V N , where N is any number greater than one, under an embodiment.
  • the DOMA can include a processing component 1502 having any number of processing paths as appropriate to form a number N of virtual microphones.
  • the DOMA of an embodiment can be coupled or connected to one or more remote devices.
  • the DOMA outputs signals to the remote devices.
  • the remote devices include, but are not limited to, at least one of cellular telephones, satellite telephones, portable telephones, wireline telephones, Internet telephones, wireless transceivers, wireless communication radios, personal digital assistants (PDAs), personal computers (PCs), headset devices, head-worn devices, and earpieces.
  • the DOMA of an embodiment can be a component or subsystem integrated with a host device.
  • the DOMA outputs signals to components or subsystems of the host device.
  • the host device includes, but is not limited to, at least one of cellular telephones, satellite telephones, portable telephones, wireline telephones, Internet telephones, wireless transceivers, wireless communication radios, personal digital assistants (PDAs), personal computers (PCs), headset devices, head- worn devices, and earpieces.
  • Figure 16 is an example of a headset or head-worn device 1600 that includes the DOMA, as described herein, under an
  • the headset 1600 of an embodiment includes a housing having two areas or receptacles (not shown) that receive and hold two microphones (e.g., Oi and 0 2 ).
  • the headset 1600 is generally a device that can be worn by a speaker 1602, for example, a headset or earpiece that positions or holds the microphones in the vicinity of the speaker's mouth.
  • the headset 1600 of an embodiment places a first physical microphone (e.g., physical microphone Oi) in a vicinity of a speaker's lips.
  • a second physical microphone e.g., physical microphone 0 2
  • the distance of an embodiment is in a range of a few centimeters behind the first physical microphone or as described herein (e.g., described with reference to Figures 11-15).
  • the DOMA is symmetric and is used in the same configuration or manner as a single close-talk microphone, but is not so limited.
  • FIG. 17 is a flow diagram for denoising 1700 acoustic signals using the DOMA, under an embodiment.
  • the denoising 1700 begins by receiving 1702 acoustic signals at a first physical microphone and a second physical microphone.
  • a first microphone signal is output from the first physical microphone and a second microphone signal is output from the second physical microphone 1704.
  • a first virtual microphone is formed 1706 by generating a first combination of the first microphone signal and the second microphone signal.
  • a second virtual microphone is formed
  • the denoising 1700 by generating a second combination of the first microphone signal and the second microphone signal, and the second combination is different from the first combination.
  • the first virtual microphone and the second virtual microphone are distinct virtual directional microphones with substantially similar responses to noise and substantially dissimilar responses to speech.
  • the denoising 1700 generates 1710 output signals by combining signals from the first virtual microphone and the second virtual microphone, and the output signals include less acoustic noise than the acoustic signals.
  • Figure 18 is a flow diagram for forming 1800 the DOMA, under an embodiment.
  • Formation 1800 of the DOMA includes forming 1802 a physical microphone array including a first physical microphone and a second physical microphone.
  • the first physical microphone outputs a first microphone signal and the second physical microphone outputs a second microphone signal.
  • a virtual microphone array is formed 1804 comprising a first virtual microphone and a second virtual microphone.
  • the first virtual microphone comprises a first combination of the first microphone signal and the second microphone signal.
  • the second virtual microphone comprises a second combination of the first microphone signal and the second microphone signal, and the second
  • the virtual microphone array including a single null oriented in a direction toward a source of speech of a human speaker.
  • VMs for the adaptive noise suppression system of an embodiment includes substantially similar noise response in ⁇ and V 2 .
  • Substantially similar noise response as used herein means that Hi(z) is simple to model and will not change much during speech, satisfying conditions R2 and R4 described above and allowing strong denoising and minimized bleedthrough.
  • the construction of VMs for the adaptive noise suppression system of an embodiment includes relatively small speech response for V 2 .
  • the relatively small speech response for V 2 means that H 2 (z) « 0, which will satisfy conditions R3 and R5 described above.
  • VMs for the adaptive noise suppression system of an embodiment further includes sufficient speech response for Vi so that the cleaned speech will have significantly higher SNR than the original speech captured by Oi.
  • omnidirectional microphones O a and 0 2 to an identical acoustic source have been normalized so that they have exactly the same response (amplitude and phase) to that source. This can be accomplished using standard microphone array methods (such as frequency-based calibration) well known to those versed in the art.
  • V 2 (z) 0 2 ( ⁇ ) - ⁇ - ⁇ ⁇ 0 1 ( ⁇ )
  • the distances di and d 2 are the distance from Oi and 0 2 to the speech source (see Figure 12), respectively, and ⁇ is their difference divided by c, the speed of sound, and multiplied by the sampling frequency f s .
  • ⁇ is in samples, but need not be an integer.
  • fractional-delay filters (well known to those versed in the art) may be used.
  • the ⁇ above is not the conventional ⁇ used to denote the mixing of VMs in adaptive beamforming; it is a physical variable of the system that depends on the intra-microphone distance d 0 (which is fixed) and the distance d s and angle ⁇ , which can vary. As shown below, for properly calibrated microphones, it is not necessary for the system to be programmed with the exact ⁇ of the array. Errors of approximately 10-15% in the actual ⁇ (i.e. the ⁇ used by the algorithm is not the ⁇ of the physical array) have been used with very little degradation in quality.
  • the algorithmic value of ⁇ may be calculated and set for a particular user or may be calculated adaptively during speech production when little or no noise is present. However, adaptation during use is not required for nominal performance.
  • the null in the linear response of virtual microphone V 2 to speech is located at 0 degrees, where the speech is typically expected to be located.
  • the linear response of V 2 to noise is devoid of or includes no null, meaning all noise sources are detected.
  • V 2 (z) has a null at the speech location and will therefore exhibit minimal response to the speech.
  • the speech null at zero degrees is not present for noise in the far field for the same microphone, as shown in Figure 20 with a noise source distance of approximately 1 meter. This insures that noise in front of the user will be detected so that it can be removed. This differs from conventional systems that can have difficulty removing noise in the direction of the mouth of the user.
  • Vi(z) can be formulated using the general form for Vi(z) :
  • V 1 (z) a A 0 1 (z) - z "dA - ⁇ ⁇ 0 2 ( ⁇ ) ⁇ ⁇ dB
  • V 2N (z) O m (z) - z ⁇ - z- ⁇ 0 1N (z)
  • Vi and V 2 above mean that for noise H ⁇ z) is: which, if the amplitude noise responses are about the same, has the form of an allpass filter. This has the advantage of being easily and accurately modeled, especially in magnitude response, satisfying R2. This formulation assures that the noise response will be as similar as possible and that the speech response will be proportional to (l- ⁇ 2 ). Since ⁇ is the ratio of the distances from Oi and 0 2 to the speech source, it is affected by the size of the array and the distance from the array to the speech source.
  • the linear response of virtual microphone j . to speech is devoid of or includes no null and the response for speech is greater than that shown in Figure 14.
  • the linear response of virtual microphone Vi to noise is devoid of or includes no null and the response is very similar to V 2 shown in Figure 15.
  • Figure 24 is a plot showing comparison of frequency responses for speech for the array of an embodiment and for a conventional cardioid microphone.
  • orientation of an embodiment, in which the main lobe of the speech response of Vi is oriented away from the speech source means that the speech sensitivity of Vi is lower than a normal directional microphone but is flat for all frequencies within approximately +-30 degrees of the axis of the array, as shown in Figure 23.
  • the speech response of Vi is approximately 0 to ⁇ 13 dB less than a normal directional microphone between approximately 500 and 7500 Hz and approximately 0 to 10+ dB greater than a directional microphone below approximately 500 Hz and above 7500 Hz for a sampling frequency of approximately 16000 Hz.
  • the superior noise suppression made possible using this system more than compensates for the initially poorer SNR.
  • the noise distance is not required to be 1 m or more, but the denoising is the best for those distances. For distances less than approximately 1 m, denoising will not be as effective due to the greater dissimilarity in the noise responses of Vi and V 2 . This has not proven to be an impediment in practical use - in fact, it can be seen as a feature. Any "noise" source that is ⁇ 10 cm away from the earpiece is likely to be desired to be captured and transmitted.
  • the speech null of V 2 means that the VAD signal is no longer a critical component.
  • the VAD's purpose was to ensure that the system would not train on speech and then subsequently remove it, resulting in speech distortion. If, however, V 2 contains no speech, the adaptive system cannot train on the speech and cannot remove it. As a result, the system can denoise all the time without fear of devoicing, and the resulting clean audio can then be used to generate a VAD signal for use in subsequent single-channel noise suppression algorithms such as spectral subtraction.
  • constraints on the absolute value of H ⁇ z) i.e. restricting it to absolute values less than two) can keep the system from fully training on speech even if it is detected. In reality, though, speech can be present due to a mis-located V 2 null and/or echoes or other phenomena, and a VAD sensor or other acoustic-only VAD is
  • ⁇ and ⁇ may be fixed in the noise suppression algorithm or they can be estimated when the algorithm indicates that speech production is taking place in the presence of little or no noise. In either case, there may be an error in the estimate of the actual ⁇ and ⁇ of the system. The following description examines these errors and their effect on the performance of the system. As above, "good performance" of the system indicates that there is sufficient denoising and minimal devoicing.
  • ⁇ ⁇ and ⁇ ⁇ denote the theoretical estimates of ⁇ and ⁇ used in the noise suppression algorithm.
  • the speech response of 0 2 is where ⁇ ⁇ and y R denote the real ⁇ and ⁇ of the physical system .
  • the differences between the theoretical and actual values of ⁇ and ⁇ can be due to mis-location of the speech source (it is not where it is assumed to be) and/or a change in air temperature (which changes the speed of sound) . Inserting the actual response of 0 2 for speech into the above equations for Vi and V 2 yields
  • ⁇ 25 ( ⁇ ) 0 18 ( ⁇ ) ⁇ ⁇ ⁇ -
  • FIG. 25 is a plot showing speech response for Vi (top, dashed) and V 2 (bottom, solid) versus B with d s assumed to be 0.1 m, under an embodiment. This plot shows the spatial null in V 2 to be relatively broad .
  • Figure 26 is a plot showing a ratio of Vi/V 2 speech responses shown in Figure 20 versus B, under an embodiment. The ratio of VI/V 2 is above 10 dB for all 0.8 ⁇ B ⁇ 1.1, and this means that the physical ⁇ of the system need not be exactly modeled for good performance.
  • the B factor can be non-unity for a variety of reasons. Either the distance to the speech source or the relative orientation of the array axis and the speech source or both can be different than expected . If both d istance and angle mismatches are included for B, then
  • the angle can vary up to approximately +-55 degrees and still result in a B less than 1.1, assuring good performance. This is a significant amount of allowable angular deviation. If there is both angular and distance errors, the equation above may be used to determine if the deviations will result in adequate performance. Of course, if the value for ⁇ ⁇ is allowed to update during speech, essentially tracking the speech source, then B can be kept near unity for almost all configurations.
  • N(Z) BZ ⁇ yd - 1
  • N(s) Be "Ds - 1.
  • ⁇ is the time difference between arrival of speech at ⁇ compared to V 2 , it can be errors in estimation of the angular location of the speech source with respect to the axis of the array and/or by temperature changes.
  • the speed of sound varies with temperature as where T is degrees Celsius. As the temperature decreases, the speed of sound also decreases.
  • Setting 20 C as a design temperature and a maximum expected temperature range to -40 C to +60 C (-40 F to 140 F).
  • the design speed of sound at 20 C is 343 m/s and the slowest speed of sound will be 307 m/s at -40 C with the fastest speed of sound 362 m/s at 60 C.
  • Set the array length (2d 0 ) to be 21 mm.
  • the resulting phase difference clearly affects high frequencies more than low.
  • Non-unity B affects the entire frequency range.
  • N(s) is below approximately -10 dB only for frequencies less than approximately 5 kHz and the response at low frequencies is much larger. Such a system would still perform well below 5 kHz and would only suffer from slightly elevated devoicing for frequencies above 5 kHz.
  • a temperature sensor may be integrated into the system to allow the algorithm to adjust ⁇ ⁇ as the temperature varies.
  • D can be non-zero
  • the speech source is not where it is believed to be - specifically, the angle from the axis of the array to the speech source is incorrect.
  • the distance to the source may be incorrect as well, but that introduces an error in B, not D.
  • the cancellation is still below -10 dB for frequencies below 6 kHz.
  • the cancellation is still below approximately -10 dB for frequencies below approximately 6 kHz, so an error of this type will not significantly affect the performance of the system.
  • ⁇ 2 is increased to approximately 45 degrees, as shown in Figure 32, the cancellation is below approximately -10 dB only for frequencies below approximately 2.8 kHz.
  • the cancellation is below -10 dB only for frequencies below about 2.8 kHz and a reduction in performance is expected.
  • the poor V 2 speech cancellation above approximately 4 kHz may result in significant devoicing for those frequencies.
  • ⁇ ⁇ ( ⁇ ) 0 1 ( ⁇ ) ⁇ ⁇ - ⁇ - ⁇ ( ⁇ ) ⁇ ( ⁇ ) ⁇ 2 ( ⁇ )
  • ⁇ 2 ( ⁇ ) ⁇ ( ⁇ ) 2 ( ⁇ ) - ⁇ - ⁇ ⁇ ( ⁇ ) ⁇ , ( ⁇ )
  • the ⁇ of the system should be fixed and as close to the real value as possible. In practice, the system is not sensitive to changes in ⁇ and errors of approximately +-5% are easily tolerated. During times when the user is producing speech but there is little or no noise, the system can train a(z) to remove as much speech as possible. This is accomplished by:
  • a simple adaptive filter can be used for a(z) so that only the relationship between the microphones is well modeled.
  • the system of an embodiment trains only when speech is being produced by the user.
  • a sensor like the SSM is invaluable in determining when speech is being produced in the absence of noise. If the speech source is fixed in position and will not vary significantly during use (such as when the array is on an earpiece), the adaptation should be infrequent and slow to update in order to minimize any errors introduced by noise present during training.
  • V 2 (z) 0 2 (z) - ⁇ " ⁇ ⁇ 2 ⁇ ⁇ ⁇ ! (z)
  • Bl and B2 are both positive numbers or zero. If Bl and B2 are set equal to unity, the optimal system results as described above. If Bl is allowed to vary from unity, the response of Vi is affected. An examination of the case where B2 is left at 1 and Bl is decreased follows. As Bl drops to approximately zero, Vi becomes less and less directional, until it becomes a simple
  • variables ⁇ and ⁇ may be introduced so that: - ⁇ )0 2 ⁇ ( ⁇ ) + (1 + A)0 1N (z)z-
  • V 2 (z) (l + ⁇ )0 2 ⁇ ( ⁇ ) + ( ⁇ - ⁇ )0 1 ⁇ (_ ⁇ ) ⁇ ⁇ ⁇
  • This formulation also allows the virtual microphone responses to be varied but retains the all-pass characteristic of H a (z).
  • the DOMA can be a component of a single system, multiple systems, and/or geographically separate systems.
  • the DOMA can also be a
  • the DOMA can be coupled to one or more other components (not shown) of a host system or a system coupled to the host system.
  • the processing system includes any collection of processor-based devices or computing devices operating together, or components of processing systems or devices, as is known in the art.
  • the processing system can include one or more of a portable computer, portable communication device operating in a communication network, and/or a network server.
  • the portable computer can be any of a number and/or combination of devices selected from among personal computers, cellular telephones, personal digital assistants, portable computing devices, and portable communication devices, but is not so limited.
  • the processing system can include components within a larger computer system.
  • AVAD Acoustic Voice Activity Detection
  • the AVAD methods and systems which i nclude algorithms or programs, use microphones to generate virtual directional microphones which have very similar noise responses and very dissimilar speech responses.
  • the ratio of the energies of the virtual microphones is then calculated over a given window size and the ratio can then be used with a variety of methods to generate a VAD signal.
  • the virtual microphones can be constructed using either a fixed or an adaptive filter.
  • the adaptive filter generally results in a more accurate and noise-robust VAD signal but requires training.
  • restrictions can be placed on the filter to ensure that it is training only on speech and not on environmental noise.
  • Figure 34 is a configuration of a two-microphone array of the AVAD with speech source S, under an embodiment.
  • the AVAD of an embodiment uses two physical microphones (Oi and O2) to form two virtual microphones (Vi and V 2 ).
  • the virtual microphones of an embodiment uses two physical microphones (Oi and O2) to form two virtual microphones (Vi and V 2 ).
  • the virtual microphones of an embodiment uses two physical microphones (Oi and O2) to form two virtual microphones (Vi and V 2 ).
  • embodiments are directional microphones, but the embodiment is not so limited.
  • the physical microphones of an embodiment include
  • V 2 is configured in such a way that it has minimal response to the speech of the user, while Vi is configured so that it does respond to the user's speech but has a very similar noise magnitude response to V 2 , as described in detail herein.
  • the PSAD VAD methods can then be used to determine when speech is taking place.
  • a further refinement is the use of an adaptive filter to further minimize the speech response of V 2 , thereby increasing the speech energy ratio used in PSAD and resulting in better overall performance of the AVAD.
  • the PSAD algorithm as described herein calculates the ratio of the energies of two directional microphones Mi and M 2 :
  • the size of R can be calculated for speech and noise by approximating the propagation of speech and noise waves as spherically symmetric sources. For these the energy of the propagating wave decreases as
  • the distance di is the distance from the acoustic source to Mi
  • d 2 is the distance from the acoustic source to M 2
  • the magnitude of R depends only on the relative distance between the microphones and the acoustic source.
  • the distances are typically a meter or more, and for speech sources, the distances are on the order of 10 cm, but the distances are not so limited. Therefore for a 2-cm array typical values of R are: d 7 12 cm
  • a better implementation is to use directional microphones where the second microphone has minimal speech response.
  • such microphones can be constructed using omnidirectional microphones Oi and 0 2 : where ⁇ ( ⁇ ) is a calibration filter used to compensate 0 2 's response so that it is the same as Oi, ⁇ ( ⁇ ) is a filter that describes the relationship between Oi and calibrated 0 2 for speech, and ⁇ is a fixed delay that depends on the size of the array.
  • ⁇ (z) is a calibration filter used to compensate 0 2 's response so that it is the same as Oi
  • ⁇ ( ⁇ ) is a filter that describes the relationship between Oi and calibrated 0 2 for speech
  • ⁇ is a fixed delay that depends on the size of the array.
  • Vi and V 2 have very similar noise response magnitudes and very dissimilar speech response magnitudes if d
  • the filter ⁇ ( ⁇ ) can be calculated using wave theory to be
  • FIG. 35 is a block diagram of V 2 construction using a fixed ⁇ ( ⁇ ), under an embodiment.
  • This fixed (or static) ⁇ works sufficiently well if the calibration filter (z) is accurate and di and d 2 are accurate for the user.
  • This fixed- ⁇ algorithm neglects important effects such as reflection, diffraction, poor array orientation (i.e. the microphones and the mouth of the user are not all on a line), and the possibility of different di and d 2 values for different users.
  • FIG. 36 is a block diagram of V 2 construction using an adaptive ⁇ ( ⁇ ), under an embodiment, where:
  • the adaptive process varies ⁇ ( ⁇ ) to minimize the output of V 2 when only speech is being received by Oi and 0 2 .
  • a small amount of noise may be tolerated with little ill effect, but it is preferred that only speech is being received when the coefficients of ⁇ ) are calculated.
  • Any adaptive process may be used; a normalized least-mean squares (NLMS) algorithm was used in the examples below.
  • the Vi can be constructed using the current value for ⁇ ( ⁇ ) or the fixed filter ⁇ ( ⁇ ) can be used for simplicity.
  • Figure 37 is a block diagram of Vi construction, under an embodiment.
  • the ratio for speech should be relatively high (e.g., greater than approximately 2) and the ratio for noise should be relatively low (e.g., less than approximately 1.1).
  • the ratio calculated will depend on both the relative energies of the speech and noise as well as the orientation of the noise and the reverberance of the environment.
  • either the adapted filter ⁇ ⁇ ) or the static filter b(z) may be used for Vi(z) with little effect on R - but it is important to use the adapted filter /?(z) in V 2 (z) for best performance.
  • Many techniques known to those skilled in the art e.g., smoothing, etc.
  • R more amenable to use in generating a VAD and the embodiments herein are not so limited.
  • the ratio R can be calculated for the entire frequency band of interest, or can be calculated in frequency subbands.
  • One effective subband discovered was 250 Hz to 1250 Hz, another was 200 Hz to 3000 Hz, but many others are possible and useful.
  • the vector of the ratio R versus time (or the matrix of R versus time if multiple subbands are used) can be used with any detection system (such as one that uses fixed and/or adaptive thresholds) to determine when speech is occurring. While many detection systems and methods are known to exist by those skilled in the art and may be used, the method described herein for generating an R so that the speech is easily discernable is novel. It is important to note that the R does not depend on the type of noise or its orientation or frequency content; R simply depends on the Vi and V 2 spatial response similarity for noise and spatial response dissimilarity for speech. In this way it is very robust and can operate smoothly in a variety of noisy acoustic environments.
  • FIG 38 is a flow diagram of acoustic voice activity detection 3800, under an embodiment.
  • the detection comprises forming a first virtual microphone by combining a first signal of a first physical microphone and a second signal of a second physical microphone 3802.
  • the detection comprises forming a filter that describes a relationship for speech between the first physical microphone and the second physical microphone 3804.
  • the detection comprises forming a second virtual microphone by applying the filter to the first signal to generate a first intermediate signal, and summing the first intermediate signal and the second signal 3806.
  • the detection comprises generating an energy ratio of energies of the first virtual microphone and the second virtual microphone 3808.
  • the detection comprises detecting acoustic voice activity of a speaker when the energy ratio is greater than a threshold value 3810.
  • the adaptation to the actual ⁇ ( ⁇ ) of the system leads to lower energy of the speech response in V 2 , and a higher ratio R.
  • the noise (far-field) magnitude response is largely unchanged by the adaptation process, so the ratio R will be near unity for accurately adapted beta.
  • the system can be trained on speech alone, or the noise should be low enough in energy so as not to affect or to have a minimal affect the training.
  • the coefficients of the filter ⁇ ( ⁇ ) of an embodiment are generally updated under the following conditions, but the embodiment is not so limited : speech is being produced (requires a relatively high SNR or other method of detection such as an Aliph Skin Surface Microphone (SSM) as described in United States Patent Application number 10/769,302, filed January 30, 2004, which is incorporated by reference herein in its entirety); no wind is detected (wind can be detected using many different methods known in the art, such as examining the microphones for uncorrelated low- frequency noise); and the current value of R is much larger than a smoothed history of R values (this ensures that training occurs only when strong speech is present).
  • SSM Aliph Skin Surface Microphone
  • an embodiment includes a further failsafe system to preclude accidental training from significantly disrupting the system.
  • the adaptive ⁇ is limited to certain values expected for speech. For example, values for di for an ear-mounted headset will normally fall between 9 and 14
  • the magnitude of the ⁇ filter can therefore be limited to between approximately 0.82 and 0.88 to preclude problems if noise is present during training. Looser limits can be used to compensate for inaccurate calibrations (the response of omnidirectional microphones is usually calibrated to one another so that their frequency response is the same to the same acoustic source - if the calibration is not completely accurate the virtual microphones may not form properly).
  • phase of the ⁇ filter can be limited to be what is expected from a speech source within +- 30 degrees from the axis of the array.
  • the maximum phase difference realized at 4 kHz is only 0.2 rad or about 11.4 degrees, a small amount, but not a negligible one. Therefore the ⁇ filter should almost linear phase, but some allowance made for
  • ⁇ is the current estimate. This limits the phase by restricting the effects of the non-center taps.
  • Other ways of limiting the phase of the beta filter are known to those skilled in the art and the algorithm presented here is not so limited.
  • Embodiments are presented herein that use both a fixed ⁇ ( ⁇ ) and an adaptive ⁇ ( ⁇ ), as described in detail above.
  • R was calculated using frequencies between 250 and 3000 Hz using a window size of 200 samples at 8 kHz.
  • the results for Vi (top plot), V 2 (middle plot), R (bottom plot, solid line, windowed using a 200 sample rectangular window at 8 kHz) and the VAD (bottom plot, dashed line) are shown in Figures 39-44.
  • Figures 39-44 demonstrate the use of a fixed beta filter ⁇ ( ⁇ ) in conditions of only noise (street and bus noise, approximately 70 dB SPL at the ear), only speech (normalized to 94 dB SPL at the mouth reference point (MRP)), and mixed noise and speech,
  • HATS Bruel & Kjaer Head and Torso Simulator
  • Figure 39 shows experimental results of the algorithm using a fixed beta when only noise is present, under an embodiment.
  • the top plot is Vi
  • the middle plot is V 2
  • the bottom plot is R (solid line) and the VAD result (dashed line) versus time.
  • the response of both Vi and V 2 are very similar, and the ratio R is very near unity for the entire sample.
  • the VAD response has occasional false positives denoted by spikes in the R plot (windows that are identified by the algorithm as containing speech when they do not), but these are easily removed using standard pulse removal algorithms and/or smoothing of the R results.
  • Figure 40 shows experimental results of the algorithm using a fixed beta when only speech is present, under an embodiment.
  • the top plot is Vi
  • the middle plot is V 2
  • the bottom plot is R (solid line) and the VAD result (dashed line) versus time.
  • the R ratio is between approximately 2 and approximately 7 on average, and the speech is easily discernable using the fixed threshold .
  • Figure 41 shows experimental results of the algorithm using a fixed beta when speech and noise is present, under an embodiment.
  • the top plot is Vi
  • the middle plot is V 2
  • the bottom plot is R (solid line) and the VAD result (dashed line) versus time.
  • the R ratio is lower than when no noise is present, but the VAD remains accurate with only a few false positives. There are more false negatives than with no noise, but the speech remains easily detectable using standard thresholding algorithms. Even in a moderately loud noise environment (Figure 41) the R ratio remains significantly above unity, and the VAD once again returns few false positives. More false negatives are observed, but these may be reduced using standard methods such as smoothing of R and allowing the VAD to continue reporting voiced windows for a few windows after R is u nder the threshold .
  • Results using the adaptive beta filter are shown in Figures 42-44.
  • the adaptive filter used was a five-tap NLMS FIR filter using the frequency band from 100 Hz to 3500 Hz.
  • a fixed filter of z '0A3 is used to filter Oi so that Oi and 0 2 are aligned for speech before the adaptive filter is calculated .
  • the adaptive filter was constrained using the methods above using a low ⁇ limit of 0.73, a high ⁇ limit of 0.98, and a phase limit ratio of 0.98. Again a fixed threshold was used to generate the VAD result from the ratio R, but in this case a threshold value of 2.5 was used since the R values using the adaptive beta filter are normally greater than when the fixed filter is used . This allows for a reduction of false positives without significantly increasing false negatives.
  • Figure 42 shows experimental results of the algorithm using an adaptive beta when only noise is present, under an embodiment.
  • the top plot is Vi
  • the middle plot is V 2
  • the bottom plot is R (solid line)
  • the VAD result (dashed line) versus time, with the y-axis expanded to 0-50.
  • Vi and V 2 are very close in energy and the R ratio is near unity. Only a single false positive was generated.
  • Figure 43 shows experimental results of the algorithm using an adaptive beta when only speech is present, under an embodiment.
  • the top plot is Vi
  • the middle plot is V 2
  • the bottom plot is (solid line) and the VAD result (dashed line) versus time, expanded to 0-50.
  • the V 2 response is greatly reduced using the adaptive beta, and the R ratio has increased from the range of approximately 2-7 to the range of
  • Figure 44 shows experimental results of the algorithm using an adaptive beta when speech and noise is present, under an embodiment.
  • the top plot is Vi
  • the middle plot is V 2
  • the bottom plot is R (solid line) and the VAD result (dashed line) versus time, with the y-axis expanded to 0-50.
  • the R ratio is again lower than when no noise is present, but this R with significant noise present results in a VAD signal that is about the same as the case using the fixed beta with no noise present. This shows that use of the adaptive beta allows the system to perform well in higher noise environments than the fixed beta.
  • the adaptive filter can outperform the fixed filter in the same noise environment.
  • the adaptive filter has proven to be significantly more sensitive to speech and less sensitive to noise.
  • Non-Acoustic Sensor Voiced Speech Activity Detection (NAVSAD) system and a Pathfinder Speech Activity Detection (PSAD) system are provided below including a Non-Acoustic Sensor Voiced Speech Activity Detection (NAVSAD) system and a Pathfinder Speech Activity Detection (PSAD) system.
  • NAVSAD Non-Acoustic Sensor Voiced Speech Activity Detection
  • PSAD Pathfinder Speech Activity Detection
  • FIG 45 is a block diagram of a NAVSAD system 4500, under an embodiment.
  • the NAVSAD system couples microphones 10 and sensors 20 to at least one processor 30.
  • the sensors 20 of an embodiment include voicing activity detectors or non-acoustic sensors.
  • the processor 30 controls subsystems including a detection subsystem 50, referred to herein as a detection algorithm, and a denoising subsystem 40. Operation of the denoising subsystem 40 is described in detail in the Related Applications.
  • the NAVSAD system works extremely well in any background acoustic noise environment.
  • FIG 46 is a block diagram of a PSAD system 4600, under an embodiment.
  • the PSAD system couples microphones 10 to at least one processor 30.
  • the processor 30 includes a detection subsystem 50, referred to herein as a detection algorithm, and a denoising subsystem 40.
  • the PSAD system is highly sensitive in low acoustic noise environments and relatively insensitive in high acoustic noise environments.
  • the PSAD can operate independently or as a backup to the NAVSAD, detecting voiced speech if the NAVSAD fails.
  • detection subsystems 50 and denoising subsystems 40 of both the NAVSAD and PSAD systems of an embodiment are algorithms controlled by the processor 30, but are not so limited.
  • embodiments of the NAVSAD and PSAD systems can include detection subsystems 50 and/or denoising subsystems 40 that comprise additional hardware, firmware, software, and/or combinations of hardware, firmware, and software. Furthermore, functions of the detection subsystems 50 and denoising subsystems 40 may be distributed across numerous components of the
  • FIG 47 is a block diagram of a denoising subsystem 4700, referred to herein as the Pathfinder system, under an embodiment.
  • the Pathfinder system is briefly described below, and is described in detail in the Related Applications. Two microphones Mic 1 and Mic 2 are used in the Pathfinder system, and Mic 1 is considered the "signal" microphone.
  • the Pathfinder system 4700 is equivalent to the NAVSAD system 4500 when the voicing activity detector (VAD) 4720 is a non-acoustic voicing sensor 20 and the noise removal subsystem 4740 includes the detection subsystem 50 and the denoising subsystem 40.
  • the Pathfinder system 4700 is equivalent to the PSAD system 4600 in the absence of the VAD 4720, and when the noise removal subsystem 4740 includes the detection subsystem 50 and the denoising subsystem 40.
  • the NAVSAD and PSAD systems support a two-level commercial approach in which (i) a relatively less expensive PSAD system supports an acoustic approach that functions in most low- to medium-noise environments, and (ii) a NAVSAD system adds a non-acoustic sensor to enable detection of voiced speech in any environment.
  • Unvoiced speech is normally not detected using the sensor, as it normally does not sufficiently vibrate human tissue.
  • detecting the unvoiced speech is not as important, as it is normally very low in energy and easily washed out by the noise. Therefore in high noise environments the unvoiced speech is unlikely to affect the voiced speech denoising.
  • Unvoiced speech information is most important in the presence of little to no noise and, therefore, the unvoiced detection should be highly sensitive in low noise situations, and insensitive in high noise situations. This is not easily accomplished, and comparable acoustic unvoiced detectors known in the art are incapable of operating under these environmental constraints.
  • the NAVSAD and PSAD systems include an array algorithm for speech detection that uses the difference in frequency content between two
  • microphones to calculate a relationship between the signals of the two microphones. This is in contrast to conventional arrays that attempt to use the time/phase difference of each microphone to remove the noise outside of an "area of sensitivity".
  • the methods described herein provide a significant advantage, as they do not require a specific orientation of the array with respect to the signal.
  • the systems described herein are sensitive to noise of every type and every orientation, unlike conventional arrays that depend on specific noise orientations. Consequently, the frequency-based arrays presented herein are unique as they depend only on the relative orientation of the two microphones themselves with no dependence on the orientation of the noise and signal with respect to the microphones. This results in a robust signal processing system with respect to the type of noise, microphones, and orientation between the noise/signal source and the microphones.
  • the systems described herein use the information derived from the Pathfinder noise suppression system and/or a non-acoustic sensor described in the Related Applications to determine the voicing state of an input signal, as described in detail below.
  • the voicing state includes silent, voiced, and unvoiced states.
  • the NAVSAD system for example, includes a non-acoustic sensor to detect the vibration of human tissue associated with speech.
  • the non-acoustic sensor of an embodiment is a General Electromagnetic Movement Sensor (GEMS) as described briefly below and in detail in the Related
  • the GEMS is a radio frequency device (2.4 GHz) that allows the detection of moving human tissue dielectric interfaces.
  • the GEMS includes an RF interferometer that uses homodyne mixing to detect small phase shifts associated with target motion. In essence, the sensor sends out weak electromagnetic waves (less than 1 milliwatt) that reflect off of whatever is around the sensor. The reflected waves are mixed with the original transmitted waves and the results analyzed for any change in position of the targets.
  • FIG 48 is a flow diagram of a detection algorithm 50 for use in detecting voiced and unvoiced speech, under an embodiment.
  • both the NAVSAD and PSAD systems of an embodiment include the detection algorithm 50 as the detection subsystem 50.
  • This detection algorithm 50 operates in real-time and, in an embodiment, operates on 20 millisecond windows and steps 10 milliseconds at a time, but is not so limited.
  • the voice activity determination is recorded for the first 10
  • milliseconds and the second 10 milliseconds functions as a "look-ahead" buffer. While an embodiment uses the 20/10 windows, alternative embodiments may use numerous other combinations of window values.
  • the non-acoustic sensor (or hereafter just the sensor) will be required to ensure good performance.
  • the speech source should be relatively louder in one designated microphone when compared to the other microphone. Tests have shown that this requirement is easily met with conventional microphones when the microphones are placed on the head, as any noise should result in an Hi with a gain near unity.
  • the NAVSAD relies on two parameters to detect voiced speech. These two parameters include the energy of the sensor in the window of interest, determined in an embodiment by the standard deviation (SD), and optionally the cross-correlation (XCORR) between the acoustic signal from microphone 1 and the sensor data.
  • SD standard deviation
  • XCORR cross-correlation
  • the SD is just one convenient way to determine the energy.
  • the SD is akin to the energy of the signal, which normally corresponds quite accurately to the voicing state, but may be susceptible to movement noise (relative motion of the sensor with respect to the human user) and/or electromagnetic noise.
  • the XCORR can be used. The XCORR is only calculated to 15 delays, which corresponds to just under 2 milliseconds at 8000 Hz.
  • the XCORR can also be useful when the sensor signal is distorted or modulated in some fashion. For example, there are sensor locations (such as the jaw or back of the neck) where speech production can be detected but where the signal may have incorrect or distorted time-based information. That is, they may not have well defined features in time that will match with the acoustic waveform. However, XCORR is more susceptible to errors from acoustic noise, and in high ( ⁇ 0 dB SNR) environments is almost useless.
  • the sensor detects human tissue motion associated with the closure of the vocal folds, so the acoustic signal produced by the closure of the folds is highly correlated with the closures. Therefore, sensor data that correlates highly with the acoustic signal is declared as speech, and sensor data that does not correlate well is termed noise.
  • the acoustic data is expected to lag behind the sensor data by about 0.1 to 0.8 milliseconds (or about 1-7 samples) as a result of the delay time due to the relatively slower speed of sound (around 330 m/s).
  • an embodiment uses a 15-sample correlation, as the acoustic wave shape varies significantly depending on the sound produced, and a larger correlation width is needed to ensure detection.
  • the SD and XCORR signals are related, but are sufficiently different so that the voiced speech detection is more reliable. For simplicity, though, either parameter may be used.
  • the values for the SD and XCORR are compared to empirical thresholds, and if both are above their threshold, voiced speech is declared. Example data is presented and described below.
  • Figures 49A, 49B, and 50 show data plots for an example in which a subject twice speaks the phrase "pop pan", under an embodiment.
  • Figure 49A plots the received GEMS signal 4902 for this utterance along with the mean correlation 4904 between the GEMS signal and the Mic 1 signal and the threshold Tl used for voiced speech detection.
  • Figure 49B plots the received GEMS signal 4902 for this utterance along with the standard deviation 4906 of the GEMS signal and the threshold T2 used for voiced speech detection.
  • Figure 50 plots voiced speech 5002 detected from the acoustic or audio signal 5008, along with the GEMS signal 5004 and the acoustic noise 5006; no unvoiced speech is detected in this example because of the heavy background babble noise 5006.
  • the thresholds have been set so that there are virtually no false negatives, and only occasional false positives.
  • a voiced speech activity detection accuracy of greater than 99% has been attained under any acoustic background noise conditions.
  • the NAVSAD can determine when voiced speech is occurring with high degrees of accuracy due to the non-acoustic sensor data.
  • the sensor offers little assistance in separating unvoiced speech from noise, as unvoiced speech normally causes no detectable signal in most non-acoustic sensors. If there is a detectable signal, the NAVSAD can be used, although use of the SD method is dictated as unvoiced speech is normally poorly correlated. In the absence of a detectable signal use is made of the system and methods of the Pathfinder noise removal algorithm in determining when unvoiced speech is occurring. A brief review of the Pathfinder algorithm is described below, while a detailed description is provided in the Related Applications.
  • the acoustic information coming into Microphone 1 is denoted by mi(n)
  • the information coming into Microphone 2 is similarly labeled m 2 (n)
  • the GEMS sensor is assumed available to determine voiced speech areas.
  • these signals are represented as Mi(z) and M 2 (z) .
  • M 1 ⁇ z) S(z) + N 2 ⁇ z)
  • Equation 1 has four unknowns and only two relationships and cannot be solved explicitly.
  • H ⁇ z can be calculated using any of the available system identification algorithms and the microphone outputs when only noise is being received .
  • the calculation can be done adaptively, so that if the noise changes significantly H i(z) can be recalculated quickly.
  • Equation 1 With a solution for one of the unknowns in Equation 1, solutions can be found for another, H 2 (z), by using the amplitude of the GEMS or similar device along with the amplitude of the two microphones.
  • M 2s ⁇ z) S ⁇ z)H 2 ⁇ z) which in turn leads to M 2 ⁇ Z)
  • H 2 (z) is usually quite small, so that H, (z)H j (z) « 1 , and
  • the PSAD system As sound waves propagate, they normally lose energy as they travel due to diffraction and dispersion. Assuming the sound waves originate from a point source and radiate isotropicaliy, their amplitude will decrease as a function of 1/r, where r is the distance from the originating point. This function of 1/r proportional to amplitude is the worst case, if confined to a smaller area the reduction will be less. However it is an adequate model for the configurations of interest, specifically the propagation of noise and speech to microphones located somewhere on the user's head.
  • Figure 51 is a microphone array for use under an embodiment of the PSAD system. Placing the microphones Mic 1 and Mic 2 in a linear array with the mouth on the array midline, the difference in signal strength in Mic 1 and Mic 2 (assuming the microphones have identical frequency responses) will be proportional to both di and Ad. Assuming a 1/r (or in this case 1/d) relationship, it is seen that where ⁇ is the difference in gain between Mic 1 and Mic 2 and therefore H-i(z), as above in Equation 2. The variable di is the distance from Mic 1 to the speech or noise source.
  • Figure 52 is a plot 5200 of ⁇ versus di for several Ad values, under an embodiment. It is clear that as Ad becomes larger and the noise source is closer, ⁇ becomes larger. The variable Ad will change depending on the orientation to the speech/noise source, from the maximum value on the array midline to zero perpendicular to the array midline. From the plot 5200 it is clear that for small Ad and for distances over approximately 30 centimeters (cm), ⁇ is close to unity.
  • the gain in this example is calculated by the sum of the absolute value of the filter coefficients. This sum is not equivalent to the gain, but the two are related in that a rise in the sum of the absolute value reflects a rise in the gain.
  • Figure 53 shows a plot 5300 of the gain parameter 5302 as the sum of the absolute values of H-i(z) and the acoustic data 5304 or audio from microphone 1.
  • the speech signal was an utterance of the phrase "pop pan", repeated twice.
  • the evaluated bandwidth included the frequency range from 2500 Hz to 3500 Hz, although 1500Hz to 2500 Hz was additionally used in practice. Note the rapid increase in the gain when the unvoiced speech is first encountered, then the rapid return to normal when the speech ends.
  • the large changes in gain that result from transitions between noise and speech can be detected by any standard signal processing techniques.
  • the standard deviation of the last few gain calculations is used, with thresholds being defined by a running average of the standard deviations and the standard deviation noise floor. The later changes in gain for the voiced speech are suppressed in this plot 5300 for clarity.
  • Figure 54 is an alternative plot 5400 of acoustic data presented in Figure 53.
  • the data used to form plot 5300 is presented again in this plot 5400, along with audio data 5404 and GEMS data 5406 without noise to make the unvoiced speech apparent.
  • the configuration of the microphones can have an effect on the change in gain associated with speech and the thresholds needed to detect speech.
  • each configuration will require testing to determine the proper thresholds, but tests with two very different microphone configurations showed the same thresholds and other parameters to work well.
  • the first microphone set had the signal microphone near the mouth and the noise microphone several centimeters away at the ear, while the second configuration placed the noise and signal microphones back-to- back within a few centimeters of the mouth.
  • the results presented herein were derived using the first microphone configuration, but the results using the other set are virtually identical, so the detection algorithm is relatively robust with respect to microphone placement.
  • NAVSAD and PSAD systems detect voiced and unvoiced speech.
  • One configuration uses the NAVSAD system (non-acoustic only) to detect voiced speech along with the PSAD system to detect unvoiced speech; the PSAD also functions as a backup to the NAVSAD system for detecting voiced speech.
  • An alternative configuration uses the NAVSAD system (non-acoustic correlated with acoustic) to detect voiced speech along with the PSAD system to detect unvoiced speech; the PSAD also functions as a backup to the NAVSAD system for detecting voiced speech.
  • Another alternative configuration uses the PSAD system to detect both voiced and unvoiced speech.
  • the "k” in “kick” has significant frequency content form 500 Hz to 4000 Hz, but a “sh” in “she” only contains significant energy from 1700-4000 Hz.
  • Voiced speech could be classified in a similar manner. For instance, an hi (“ee”) has significant energy around 300 Hz and 2500 Hz, and an /a/ (“ah”) has energy at around 900 Hz and 1200 Hz. This ability to discriminate unvoiced and voiced speech in the presence of noise is, thus, very useful.
  • acoustic vibration sensor also referred to as a speech sensing device
  • the acoustic vibration sensor is similar to a microphone in that it captures speech information from the head area of a human talker or talker in noisy environments. Previous solutions to this problem have either been vulnerable to noise, physically too large for certain applications, or cost prohibitive.
  • the acoustic vibration sensor described herein accurately detects and captures speech vibrations in the presence of substantial airborne acoustic noise, yet within a smaller and cheaper physical package.
  • the noise-immune speech information provided by the acoustic vibration sensor can subsequently be used in downstream speech processing applications (speech enhancement and noise suppression, speech encoding, speech recognition, talker verification, etc.) to improve the performance of those applications.
  • Figure 55 is a cross section view of an acoustic vibration sensor 5500, also referred to herein as the sensor 5500, under an embodiment.
  • Figure 56A is an exploded view of an acoustic vibration sensor 5500, under the
  • FIG. 56B is perspective view of an acoustic vibration sensor 5500, under the embodiment of Figure 55.
  • the sensor 5500 includes an enclosure 5502 having a first port 5504 on a first side and at least one second port 5506 on a second side of the enclosure 5502.
  • a diaphragm 5508 also referred to as a sensing diaphragm 5508, is positioned between the first and second ports.
  • a coupler 5510 also referred to as the shroud 5510 or cap 5510, forms an acoustic seal around the enclosure 5502 so that the first port 5504 and the side of the diaphragm facing the first port 5504 are isolated from the airborne acoustic environment of the human talker.
  • the coupler 5510 of an embodiment is contiguous, but is not so limited.
  • the second port 5506 couples a second side of the diaphragm to the external environment.
  • the sensor also includes electret material 5520 and the associated components and electronics coupled to receive acoustic signals from the talker via the coupler 5510 and the diaphragm 5508 and convert the acoustic signals to electrical signals representative of human speech. Electrical contacts 5530 provide the electrical signals as an output. Alternative embodiments can use any type/combination of materials and/or electronics to convert the acoustic signals to electrical signals representative of human speech and output the electrical signals.
  • the coupler 5510 of an embodiment is formed using materials having acoustic impedances matched to the impedance of human skin (characteristic acoustic impedance of skin is approximately 1.5xl0 6 Pa x s/m).
  • the coupler 5510 therefore, is formed using a material that includes at least one of silicone gel, dielectric gel, thermoplastic elastomers (TPE), and rubber compounds, but is not so limited.
  • TPE thermoplastic elastomers
  • the coupler 5510 of an embodiment is formed using Kraiburg TPE products.
  • the coupler 5510 of an embodiment is formed using Sylgard® Silicone products.
  • the coupler 5510 of an embodiment includes a contact device 5512 that includes, for example, a nipple or protrusion that protrudes from either or both sides of the coupler 5510.
  • a contact device 5512 that protrudes from both sides of the coupler 5510 includes one side of the contact device 5512 that is in contact with the skin surface of the talker and another side of the contact device 5512 that is in contact with the diaphragm, but the embodiment is not so limited.
  • the coupler 5510 and the contact device 5512 can be formed from the same or different materials.
  • the coupler 5510 transfers acoustic energy efficiently from skin/flesh of a talker to the diaphragm, and seals the diaphragm from ambient airborne acoustic signals.
  • the coupler 5510 with the contact device 5512 efficiently transfers acoustic signals directly from the talker's body (speech vibrations) to the diaphragm while isolating the diaphragm from acoustic signals in the airborne environment of the talker (characteristic acoustic impedance of air is approximately 415 Pa x s/m).
  • the diaphragm is isolated from acoustic signals in the airborne environment of the talker by the coupler 5510 because the coupler 5510 prevents the signals from reaching the diaphragm, thereby reflecting and/or dissipating much of the energy of the acoustic signals in the airborne environment.
  • the sensor 5500 responds primarily to acoustic energy transferred from the skin of the talker, not air.
  • the sensor 5500 picks up speech-induced acoustic signals on the surface of the skin while airborne acoustic noise signals are largely rejected, thereby increasing the signal-to- noise ratio and providing a very reliable source of speech information.
  • Performance of the sensor 5500 is enhanced through the use of the seal provided between the diaphragm and the airborne environment of the talker.
  • the seal is provided by the coupler 5510.
  • a modified gradient microphone is used in an embodiment because it has pressure ports on both ends. Thus, when the first port 5504 is sealed by the coupler 5510, the second port 5506 provides a vent for air movement through the sensor 5500.
  • Figure 57 is a schematic diagram of a coupler 5510 of an acoustic vibration sensor, under the embodiment of Figure 55.
  • the dimensions shown are in millimeters and are only intended to serve as an example for one embodiment. Alternative embodiments of the coupler can have different configurations and/or dimensions.
  • the dimensions of the coupler 5510 show that the acoustic vibration sensor 5500 is small in that the sensor 5500 of an embodiment is approximately the same size as typical microphone capsules found in mobile communication devices.
  • This small form factor allows for use of the sensor 5510 in highly mobile miniaturized applications, where some example applications include at least one of cellular telephones, satellite telephones, portable telephones, wireline telephones, Internet telephones, wireless transceivers, wireless communication radios, personal digital assistants (PDAs), personal computers (PCs), headset devices, head-worn devices, and earpieces.
  • PDAs personal digital assistants
  • PCs personal computers
  • the acoustic vibration sensor provides very accurate Voice Activity
  • VAD Voice Activity Detection
  • processing applications including but not limited to: noise suppression algorithms such as the Pathfinder algorithm available from Aliph, Brisbane, California and described in the Related Applications; speech compression algorithms such as the Enhanced Variable Rate Coder (EVRC) deployed in many commercial systems; and speech recognition systems.
  • noise suppression algorithms such as the Pathfinder algorithm available from Aliph, Brisbane, California and described in the Related Applications
  • speech compression algorithms such as the Enhanced Variable Rate Coder (EVRC) deployed in many commercial systems
  • EVRC Enhanced Variable Rate Coder
  • the acoustic vibration sensor uses only minimal power to operate (on the order of 200 micro Amps, for example).
  • the acoustic vibration sensor uses a standard microphone interface to connect with signal processing devices. The use of the standard microphone interface avoids the additional expense and size of interface circuitry in a host device and supports for of the sensor in highly mobile applications where power usage is an issue.
  • Figure 58 is an exploded view of an acoustic vibration sensor 5800, under an alternative embodiment.
  • the sensor 5800 includes an enclosure 5802 having a first port 5804 on a first side and at least one second port (not shown) on a second side of the enclosure 5802.
  • a diaphragm 5808 is positioned between the first and second ports.
  • a layer of silicone gel 5809 or other similar substance is formed in contact with at least a portion of the diaphragm 5808.
  • a coupler 5810 or shroud 5810 is formed around the enclosure 5802 and the silicon gel 5809 where a portion of the coupler 5810 is in contact with the silicon gel 5809.
  • the coupler 5810 and silicon gel 5809 in combination form an acoustic seal around the enclosure 5802 so that the first port 5804 and the side of the diaphragnn facing the first port 5804 are isolated from the acoustic environment of the human talker.
  • the second port couples a second side of the diaphragm to the acoustic environment.
  • the senor includes additional electronic materials as appropriate that couple to receive acoustic signals from the talker via the coupler 5810, the silicon gel 5809, and the diaphragm 5808 and convert the acoustic signals to electrical signals representative of human speech.
  • Alternative embodiments can use any type/combination of materials and/or electronics to convert the acoustic signals to electrical signals representative of human speech.
  • the coupler 5810 and/or gel 5809 of an embodiment are formed using materials having impedances matched to the impedance of human skin.
  • the coupler 5810 is formed using a material that includes at least one of silicone gel, dielectric gel, thermoplastic elastomers (TPE), and rubber compounds, but is not so limited.
  • TPE thermoplastic elastomers
  • the coupler 5810 transfers acoustic energy efficiently from skin/flesh of a talker to the diaphragm, and seals the diaphragm from ambient airborne acoustic signals.
  • the coupler 5810 efficiently transfers acoustic signals directly from the talker's body (speech vibrations) to the diaphragm while isolating the diaphragm from acoustic signals in the airborne environment of the talker.
  • the diaphragm is isolated from acoustic signals in the airborne environment of the talker by the silicon gel 5809/coupler 5810 because the silicon gel 5809/coupler 5810 prevents the signals from reaching the diaphragm, thereby reflecting and/or dissipating much of the energy of the acoustic signals in the airborne environment.
  • the senor 5800 responds primarily to acoustic energy
  • the acoustic vibration sensor can detect skin vibrations associated with the production of speech.
  • the sensor can be mounted in a device, handset, or earpiece in any manner, the only restriction being that reliable skin contact is used to detect the skin-borne vibrations associated with the production of speech .
  • Figure 59 shows representative areas of sensitivity 5900-5920 on the human head appropriate for placement of the acoustic vibration sensor 5500/5800, under an embodiment.
  • the areas of sensitivity 5900-5920 include numerous locations 5902-5908 in an area behind the ear 5900, at least one location 5912 in an area in front of the ear 5910, and in numerous locations 5922-5928 in the ear canal area 5920.
  • the areas of sensitivity 5900-5920 are the same for both sides of the human head. These representative areas of sensitivity 5900-5920 are provided as examples only and do not limit the embodiments described herein to use in these areas.
  • Figure 60 is a generic headset device 6000 that includes an acoustic vibration sensor 5500/5800 placed at any of a number of locations 6002-6010, under an embodiment.
  • placement of the acoustic vibration sensor 5500/5800 can be on any part of the device 6000 that corresponds to the areas of sensitivity 5900-5920 ( Figure 59) on the human head.
  • a headset device is shown as an example, any number of communication devices known in the art can carry and/or couple to an acoustic vibration sensor 5500/5800.
  • Figure 61 is a diagram of a manufacturing method 6100 for an acoustic vibration sensor, under an embodiment. Operation begins with, for example, a uni-directional microphone 6120, at block 6102. Silicon gel 6122 is formed over/on the diaphragm (not shown) and the associated port, at block 6104. A material 6124, for example polyurethane film, is formed or placed over the microphone 6120/silicone gel 6122 combination, at block 6106, to form a coupler or shroud. A snug fit collar or other device is placed on the microphone to secure the material of the coupler during curing, at block 6108.
  • a uni-directional microphone 6120 at block 6102.
  • Silicon gel 6122 is formed over/on the diaphragm (not shown) and the associated port, at block 6104.
  • a material 6124 for example polyurethane film, is formed or placed over the microphone 6120/silicone gel 6122 combination, at block 6106, to form a coupler or shroud.
  • the silicon gel (block 6102) is an optional component that depends on the embodiment of the sensor being manufactured, as described above. Consequently, the manufacture of an acoustic vibration sensor 5500 that includes a contact device 5512 (referring to Figure 55) will not include the formation of silicon gel 6122 over/on the diaphragm. Further, the coupler formed over the microphone for this sensor 5500 will include the contact device 5512 or formation of the contact device 5512.
  • the embodiments described herein include a system comprising a first detector that receives a first signal and a second detector that receives a second signal.
  • the system of an embodiment comprises a voice activity detector (VAD) coupled to the first detector.
  • VAD voice activity detector
  • the system of an embodiment comprises a wind detector coupled to the second detector.
  • the wind detector correlates signals received at the second detector and derives from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector.
  • the wind detector controls a configuration of the second detector according to the plurality of wind metrics.
  • the wind detector uses the plurality of wind metrics to dynamically control mixing of the first signal and the second signal to generate an output signal for transmission.
  • the embodiments described herein include a system comprising : a first detector that receives a first signal and a second detector that receives a second signal; a voice activity detector (VAD) coupled to the first detector, the VAD generating a VAD signal when the first signal corresponds to voiced speech; and a wind detector coupled to the second detector, wherein the wind detector correlates signals received at the second detector and derives from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector, wherein the wind detector controls a configuration of the second detector according to the plurality of wind metrics, wherein the wind detector uses the plurality of wind metrics to dynamically control mixing of the first signal and the second signal to generate an output signal for transmission.
  • VAD voice activity detector
  • the first detector of an embodiment is a vibration sensor.
  • the first detector of an embodiment is a skin surface microphone (SSM).
  • the second detector of an embodiment is an acoustic sensor.
  • the second detector of an embodiment comprises two omnidirectional microphones.
  • the two omnidirectional microphones of an embodiment are positioned adjacent one another and are separated by a distance approximately in a range of 10 millimeters (mm) to 40 mm.
  • the wind detector of an embodiment comprises an adaptive filter coupled to the second detector, wherein the wind detector correlates signals by calculating energy of an adaptive filter error.
  • the wind detector of an embodiment comprises a first exponential averaging filter and a second exponential averaging filter coupled to the adaptive filter, wherein the wind detector applies the energy to the first exponential averaging filter and the second exponential averaging filter.
  • the wind detector of an embodiment generates an instantaneous wind level from the energy, wherein the instantaneous wind level represents an instant wind level of the wind noise.
  • the plurality of wind metrics of an embodiment comprise a wind present metric that characterizes the instantaneous wind level relative to a present wind threshold over which the wind noise negatively affects electronic operations in a host electronic system.
  • the plurality of wind metrics of an embodiment comprise a wind mode metric that characterizes the instantaneous wind level relative to a wind high threshold over which the wind noise is considered to have a relatively high impact on audio intelligibility in a host electronic system.
  • the wind detector of an embodiment generates a current wind level from the energy, wherein the current wind level represents an average current wind level of the wind noise.
  • the plurality of wind metrics of an embodiment comprise a wind index metric that characterizes the current wind level relative to a minimum wind threshold under which the wind noise is considered to have a negligible impact on noise suppression and audio intelligibility in a host electronic system.
  • the plurality of wind metrics of an embodiment comprise a wind mode metric that the wind detector generates to control the configuration of the second detector, wherein the wind mode metric characterizes instantaneous wind level relative to a wind high threshold over which the wind noise is considered to have a relatively high impact on audio intelligibility in a host electronic system.
  • the wind detector of an embodiment controls the configuration of the second detector by controlling generation of a summed detector signal by summing signals from each of two microphones of the second detector.
  • the wind detector of an embodiment controls the configuration of the second detector by controlling separate processing of signals from each of two microphones of the second detector when the wind mode metric indicates instantaneous wind level is below the wind high threshold.
  • the wind detector of an embodiment controls the configuration of the second detector by controlling application of dual-microphone noise suppression to the signals from the two microphones.
  • the system of an embodiment comprises a gain controller coupled to the first detector and the wind detector.
  • the gain controller of an embodiment controls gain applied to the first signal in response to the plurality of wind metrics and the VAD signal.
  • the gain controller of an embodiment adjusts a gain applied to the first signal when the plurality of wind metrics indicates no wind is present.
  • the plurality of wind metrics of an embodiment includes a wind present metric that characterizes an instantaneous wind level derived from the second signal relative to a present wind threshold over which the wind noise negatively affects electronic operations in a host electronic system.
  • the system of an embodiment comprises adjusting the gain to match a first root mean square (RMS) of the first signal to a second RMS of a noise- suppressed speech signal.
  • the system of an embodiment comprises generating a VAD signal when the first signal corresponds to voiced speech, and using the VAD signal to noise gate the first signal.
  • RMS root mean square
  • the gain controller of an embodiment adjusts a gain applied to the first signal when the VAD signal indicates the first signal corresponds to voiced speech.
  • the system of an embodiment comprises a first filter coupled to the first detector and a second filter coupled to the second detector.
  • the first filter of an embodiment is a low-pass filter and the second filter is a high-pass filter.
  • the plurality of wind metrics of an embodiment dynamically control mixing of the first signal and the second signal.
  • the plurality of wind metrics of an embodiment dynamically adjust a response of the first filter to which the first signal is applied and dynamically adjust a response of the second filter to which the second signal is applied.
  • the plurality of wind metrics of an embodiment comprise a wind index metric that characterizes a current wind level relative to a minimum wind threshold under which the wind noise is considered to have a negligible impact on noise suppression and audio intelligibility in a host electronic system, wherein the current wind level represents an average current wind level of the wind noise.
  • the system of an embodiment comprises estimating a wind frequency response of the wind noise from the wind index metric.
  • the system of an embodiment comprises a comfort equalizer coupled to the second detector.
  • the comfort equalizer of an embodiment generates a comfort wind component and adds the comfort wind component to audio signals, wherein the comfort wind component provides listener awareness of wind presence.
  • the comfort equalizer of an embodiment is coupled to a transmitter, and adds the comfort wind component to audio signals processed for transmission.
  • the comfort equalizer of an embodiment is coupled to a receiver, and adds the comfort wind component to audio signals processed for reception.
  • the comfort equalizer of an embodiment generates the comfort wind component by subtracting signals from each of two microphones of the second detector to generate a difference signal.
  • the system of an embodiment comprises modulating the difference signal by a gain to generate a modulated signal.
  • the gain of an embodiment comprises a static gain that provides an appropriate level of wind noise feedback in a loudspeaker.
  • the gain of an embodiment comprises a gating factor derived from a wind present metric output by the wind detector, wherein the wind present metric characterizes an instantaneous wind level derived from the second signal relative to a present wind threshold over which the wind noise negatively affects electronic operations in a host electronic system.
  • the system of an embodiment comprises filtering the modulated signal to provide the comfort wind component, the filtering comprising limiting an amount of low-frequency wind noise and high-frequency wind noise.
  • the embodiments described herein include a system comprising a first detector that receives a first signal and a second detector that receives a second signal.
  • the system of an embodiment comprises a voice activity detector (VAD) coupled to the first detector.
  • VAD voice activity detector
  • the VAD generates a VAD signal when the first signal corresponds to voiced speech.
  • the embodiment comprises a wind detector coupled to the second detector.
  • the wind detector correlates signals received at the second detector and derives from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector.
  • the wind detector uses the plurality of wind metrics to dynamically control mixing of the first signal and the second signal to generate an output signal for transmission.
  • the embodiments described herein include a system comprising : a first detector that receives a first signal and a second detector that receives a second signal; a voice activity detector (VAD) coupled to the first detector, the VAD generating a VAD signal when the first signal corresponds to voiced speech; and a wind detector coupled to the second detector, wherein the wind detector correlates signals received at the second detector and derives from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector, wherein the wind detector uses the plurality of wind metrics to dynamically control mixing of the first signal and the second signal to generate an output signal for transmission.
  • VAD voice activity detector
  • the embodiments described herein include a system comprising a first detector that receives a first signal and a second detector that receives a second signal.
  • the system of an embodiment comprises a voice activity detector (VAD) coupled to the first detector.
  • VAD voice activity detector
  • the VAD generates a VAD signal when the first signal corresponds to voiced speech.
  • the system of an embodiment comprises a wind detector coupled to the second detector.
  • the wind detector correlates signals received at the second detector and derives from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector.
  • the wind detector controls a configuration of the second detector according to the plurality of wind metrics.
  • the embodiments described herein include a system comprising : a first detector that receives a first signal and a second detector that receives a second signal; a voice activity detector (VAD) coupled to the first detector, the VAD generating a VAD signal when the first signal corresponds to voiced speech; and a wind detector coupled to the second detector, wherein the wind detector correlates signals received at the second detector and derives from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector, wherein the wind detector controls a configuration of the second detector according to the plurality of wind metrics.
  • VAD voice activity detector
  • the embodiments described herein include a method comprising receiving a first signal at a first detector and a second signal at a second detector.
  • the method of an embodiment comprises determining a correlation between signals received at the second detector and deriving from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector.
  • the method of an embodiment comprises controlling a configuration of the second detector according to the plurality of wind metrics.
  • the method of an embodiment comprises generating an output signal for transmission by dynamically mixing the first signal and the second signal according to the plurality of wind metrics.
  • the first detector of an embodiment is a vibration sensor.
  • the first detector of an embodiment is a skin surface microphone (SSM).
  • SSM skin surface microphone
  • the second detector of an embodiment is an acoustic sensor.
  • the second detector of an embodiment comprises two omnidirectional microphones.
  • omnidirectional microphones adjacent one another and separating the two omnidirectional microphones by a distance approximately in a range of 10 millimeters (mm) to 40 mm.
  • Determining the correlation of an embodiment comprises calculating energy of an adaptive filter error.
  • the method of an embodiment comprises applying the energy to a first exponential averaging filter and a second exponential averaging filter.
  • the method of an embodiment comprises deriving an instantaneous wind level from the energy, wherein the instantaneous wind level represents an instant wind level of the wind noise.
  • the plurality of wind metrics of an embodiment comprise a wind present metric that characterizes the instantaneous wind level relative to a present wind threshold over which the wind noise negatively affects electronic operations in a host electronic system.
  • the plurality of wind metrics of an embodiment comprise a wind mode metric that characterizes the instantaneous wind level relative to a wind high threshold over which the wind noise is considered to have a relatively high impact on audio intelligibility in a host electronic system.
  • the method of an embodiment comprises deriving a current wind level from the energy, wherein the current wind level represents an average current wind level of the wind noise.
  • the plurality of wind metrics of an embodiment comprise a wind index metric that characterizes the current wind level relative to a minimum wind threshold under which the wind noise is considered to have a negligible impact on noise suppression and audio intelligibility in a host electronic system.
  • the method of an embodiment comprises controlling a gain applied to the first signal in response to the plurality of wind metrics and a voice activity detection (VAD) signal.
  • VAD voice activity detection
  • the method of an embodiment comprises adjusting the gain when the plurality of wind metrics indicates no wind is present.
  • the plurality of wind metrics of an embodiment is a wind present metric that characterizes an instantaneous wind level derived from the second signal relative to a present wind threshold over which the wind noise negatively affects electronic operations in a host electronic system.
  • VAD signal indicates the first signal corresponds to voiced speech.
  • the method of an embodiment comprises adjusting the gain to match a first root mean square (RMS) of the first signal to a second RMS of a noise- suppressed speech signal.
  • RMS root mean square
  • the method of an embodiment comprises generating a VAD signal when the first signal corresponds to voiced speech, and using the VAD signal to noise gate the first signal.
  • the controlling of the configuration of the second detector of an embodiment according to the plurality of wind metrics comprises use of a wind mode metric that characterizes instantaneous wind level relative to a wind high threshold over which the wind noise is considered to have a relatively high impact on audio intelligibility in a host electronic system.
  • the controlling of the configuration of the second detector of an embodiment comprises, when the wind mode metric indicates instantaneous wind level exceeds the wind high threshold, generating a summed detector signal by summing signals from each of two microphones of the second detector.
  • the controlling of the configuration of the second detector of an embodiment comprises applying single-microphone noise suppression to the summed detector signal.
  • the controlling of the configuration of the second detector of an embodiment comprises separately processing signals from each of two microphones of the second detector when the wind mode metric indicates instantaneous wind level is below the wind high threshold.
  • the controlling of the configuration of the second detector of an embodiment comprises applying dual-microphone noise suppression to the signals from the two microphones.
  • embodiment according to the plurality of wind metrics comprises dynamically adjusting a response of a first filter to which the first signal is applied and dynamically adjusting a response of a second filter to which the second signal is applied.
  • the first filter of an embodiment is a low-pass filter and the second filter of an embodiment is a high-pass filter.
  • the plurality of wind metrics of an embodiment is a wind index metric that characterizes a current wind level relative to a minimum wind threshold under which the wind noise is considered to have a negligible impact on noise suppression and audio intelligibility in a host electronic system, wherein the current wind level represents an average current wind level of the wind noise.
  • the method of an embodiment comprises estimating a wind frequency response of the wind noise from the wind index metric.
  • the method of an embodiment comprises generating a comfort wind component and adding the comfort wind component to receive and transmit audio, wherein the comfort wind component provides listener awareness of wind presence.
  • the method of an embodiment comprises generating the comfort wind component by subtracting signals from each of two microphones of the second detector to generate a difference signal.
  • the method of an embodiment comprises modulating the difference signal by a gain to generate a modulated signal.
  • the gain of an embodiment comprises a static gain that provides an appropriate level of wind noise feedback in a loudspeaker.
  • the gain of an embodiment comprises a gating factor derived from a wind present metric that characterizes an instantaneous wind level derived from the second signal relative to a present wind threshold over which the wind noise negatively affects electronic operations in a host electronic system.
  • the method of an embodiment comprises filtering the modulated signal to provide the comfort wind component, the filtering comprising limiting an amount of low-frequency wind noise and high-frequency wind noise reaching a receiver.
  • the embodiments described herein include a method comprising receiving a first signal at a first detector and a second signal at a second detector.
  • the method of an embodiment comprises determining a correlation between signals received at the second detector and deriving from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector.
  • the method of an embodiment comprises controlling a configuration of the second detector according to the plurality of wind metrics.
  • the embodiments described herein include a method comprising receiving a first signal at a first detector and a second signal at a second detector.
  • the method of an embodiment comprises determining a correlation between signals received at the second detector and deriving from the correlation a plurality of wind metrics that characterize wind noise that is acoustic disturbance corresponding to at least one of air flow and air pressure in the second detector.
  • the method of an embodiment comprises generating an output signal for transmission by dynamically mixing the first signal and the second signal according to the plurality of wind metrics.
  • the systems and methods described herein include and/or run under and/or in association with a processing system.
  • the processing system includes any collection of processor-based devices or computing devices operating together, or components of processing systems or devices, as is known in the art.
  • the processing system can include one or more of a portable computer, portable communication device operating in a communication network, and/or a network server.
  • the portable computer can be any of a number and/or combination of devices selected from among personal computers, cellular telephones, personal digital assistants, portable computing devices, and portable communication devices, but is not so limited.
  • the processing system can include components within a larger computer system.
  • the processing system of an embodiment includes at least one processor and at least one memory device or subsystem.
  • the processing system can also include or be coupled to at least one database.
  • the term "processor” as generally used herein refers to any logic processing unit, such as one or more central processing units (CPUs), digital signal processors (DSPs), application- specific integrated circuits (ASIC), etc.
  • the processor and memory can be monolithically integrated onto a single chip, distributed among a number of chips or components of a host system, and/or provided by some combination of algorithms.
  • the methods described herein can be implemented in one or more of software algorithm(s), programs, firmware, hardware, components, circuitry, in any combination.
  • System components embodying the systems and methods described herein can be located together or in separate locations. Consequently, system components embodying the systems and methods described herein can be components of a single system, multiple systems, and/or geographically separate systems. These components can also be subcomponents or
  • subsystems of a single system, multiple systems, and/or geographically separate systems can be coupled to one or more other components of a host system or a system coupled to the host system.
  • Communication paths couple the system components and include any medium for communicating or transferring files among the components.
  • the communication paths include wireless connections, wired connections, and hybrid wireless/wired connections.
  • the communication paths also include couplings or connections to networks including local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), proprietary networks, interoffice or backend networks, and the Internet.
  • LANs local area networks
  • MANs metropolitan area networks
  • WANs wide area networks
  • proprietary networks interoffice or backend networks
  • the Internet and the Internet.
  • the communication paths include removable fixed mediums like floppy disks, hard disk drives, and CD-ROM disks, as well as flash RAM, Universal Serial Bus (USB) connections, RS-232 connections, telephone lines, buses, and electronic mail messages.
  • USB Universal Serial Bus

Abstract

L'invention porte sur des systèmes et sur des procédés qui permettent de réduire l'impact négatif du vent sur un système électronique et qui comprennent l'utilisation d'un premier détecteur qui reçoit un premier signal et d'un second détecteur qui reçoit un second signal. Un détecteur d'activité vocale (VAD) couplé au premier détecteur génère un signal VAD lorsque le premier signal correspond à une parole voisée. Un détecteur de vent couplé au second détecteur établit une corrélation entre les signaux reçus au niveau du second détecteur et déduit de la corrélation des mesures de vent qui caractérisent le bruit du vent qui est une perturbation acoustique correspondant à un écoulement d'air et/ou une pression d'air dans le second détecteur. Le détecteur de vent commande une configuration du second détecteur selon les mesures de vent. Le détecteur de vent utilise les mesures de vent pour commander dynamiquement le mélange du premier signal et du second signal afin de générer un signal de sortie pour une transmission.
EP11778185.6A 2010-05-03 2011-05-03 Composant de suppression/remplacement du vent à utiliser avec des systèmes électroniques Withdrawn EP2567377A4 (fr)

Applications Claiming Priority (3)

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US12/772,963 US8452023B2 (en) 2007-05-25 2010-05-03 Wind suppression/replacement component for use with electronic systems
US12/772,975 US8488803B2 (en) 2007-05-25 2010-05-03 Wind suppression/replacement component for use with electronic systems
PCT/US2011/035029 WO2011140110A1 (fr) 2010-05-03 2011-05-03 Composant de suppression/remplacement du vent à utiliser avec des systèmes électroniques

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EP2567377A4 EP2567377A4 (fr) 2016-10-12

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CN (1) CN203242334U (fr)
AU (1) AU2011248297A1 (fr)
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WO (1) WO2011140110A1 (fr)

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EP2567377A4 (fr) 2016-10-12

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