WO2010048635A1 - Détection acoustique d’activité vocale (avad) pour systèmes électroniques - Google Patents

Détection acoustique d’activité vocale (avad) pour systèmes électroniques Download PDF

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Publication number
WO2010048635A1
WO2010048635A1 PCT/US2009/062129 US2009062129W WO2010048635A1 WO 2010048635 A1 WO2010048635 A1 WO 2010048635A1 US 2009062129 W US2009062129 W US 2009062129W WO 2010048635 A1 WO2010048635 A1 WO 2010048635A1
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Prior art keywords
microphone
signal
speech
filter
physical
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PCT/US2009/062129
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English (en)
Inventor
Nicolas Petit
Gregory Burnett
Zhinian Jing
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Aliphcom, Inc.
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Application filed by Aliphcom, Inc. filed Critical Aliphcom, Inc.
Priority to CA2741652A priority Critical patent/CA2741652A1/fr
Priority to AU2009308442A priority patent/AU2009308442A1/en
Priority to CN2009801515125A priority patent/CN102282865A/zh
Priority to EP09822855.4A priority patent/EP2353302A4/fr
Publication of WO2010048635A1 publication Critical patent/WO2010048635A1/fr

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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; 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 TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques 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
    • 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

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.
  • Non-acoustic methods have been employed successfully in commercial products such as the Jawbone headset produced by Aliphcom, Inc., San Francisco, California (Aliph), but an acoustic-only solution is desired in some cases (e.g., for reduced cost, as a supplement to the non-acoustic sensor, etc.).
  • Figure 1 is a configuration of a two-microphone array with speech source S, under an embodiment.
  • Figure 2 is a block diagram of V 2 construction using a fixed ⁇ (z), under an embodiment.
  • Figure 3 is a block diagram Of V 2 construction using an adaptive ⁇ (z), under an embodiment.
  • Figure 4 is a block diagram of Vi construction, under an embodiment.
  • Figure 5 is a flow diagram of acoustic voice activity detection, under an embodiment.
  • Figure 6 shows experimental results of the algorithm using a fixed beta when only noise is present, under an embodiment.
  • Figure 7 shows experimental results of the algorithm using a fixed beta when only speech is present, under an embodiment.
  • Figure 8 shows experimental results of the algorithm using a fixed beta when speech and noise is present, under an embodiment.
  • Figure 9 shows experimental results of the algorithm using an adaptive beta when only noise is present, under an embodiment.
  • Figure 10 shows experimental results of the algorithm using an adaptive beta when only speech is present, under an embodiment.
  • Figure 11 shows experimental results of the algorithm using an adaptive beta when speech and noise is present, under an embodiment.
  • Figure 12 is a block diagram of a NAVSAD system, under an embodiment.
  • Figure 13 is a block diagram of a PSAD system, under an embodiment,
  • Figure 14 is a block diagram of a denoising subsystem, referred to herein as the Pathfinder system, under an embodiment
  • Figure 15 is a flow diagram of a detection algorithm for use in detecting voiced and unvoiced speech, under an embodiment, Figures 16A, 16B, and 17 show data plots for an example in which a subject twice speaks the phrase "pop pan", under an embodiment
  • Figure 16A plots the received GEMS signal for this utterance along with the mean correlation between the GEMS signal and the Mic 1 signal and the threshold T1 used for voiced speech detection, under an embodiment.
  • Figure 16B plots the received GEMS signal for this utterance along with the standard deviation of the GEMS signal and the threshold T2 used for voiced speech detection, under an embodiment.
  • Figure 17 plots voiced speech detected from the acoustic or audio signal, along with the GEMS signal and the acoustic noise; no unvoiced speech is detected in this example because of the heavy background babble noise, under an embodiment.
  • Figure 18 is a microphone array for use under an embodiment of the PSAD system.
  • Figure 19 is a plot of ⁇ M versus di for several ⁇ d values
  • Figure 20 shows a plot of the gain parameter as the sum of the absolute values of Hi(z) and the acoustic data or audio from microphone 1
  • Figure 21 is an alternative plot of acoustic data presented in Figure 20, under an embodiment
  • Figure 22 is a two-microphone adaptive noise suppression system, under an embodiment.
  • Figure 23 is a generalized two-microphone array (DOMA) including an array and speech source S configuration, under an embodiment,
  • DOMA generalized two-microphone array
  • Figure 24 is a system for generating or producing a first order gradient microphone V using two omnidirectional elements O 1 and O 2 , under an embodiment.
  • Figure 25 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 26 is a block diagram for a DOMA 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.
  • Figure 27 is an example of a headset or head-worn device that includes the DOMA, as described herein, under an embodiment
  • Figure 28 is a flow diagram for denoising acoustic signals using the DOMA, under an embodiment
  • Figure 29 is a flow diagram for forming the DOMA, under an embodiment
  • Figure 35 is a plot showing comparison of frequency responses for speech for the array of an embodiment and for a conventional cardioid microphone, under an embodiment
  • Figure 36 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, under an embodiment,
  • Figure 37 is a plot showing a ratio Of V 1 ZV 2 speech responses shown in Figure 31 versus B, under an embodiment
  • Figure 40 is a plot of amplitude (top) and phase (bottom) response of N(s) with
  • HATS Bruel and Kjaer Head and Torso Simulator
  • the AVAD methods and systems which include 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. In addition, restrictions can be placed on the filter to ensure that it is training only on speech and not on environmental noise.
  • Figure 1 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 O 2 ) to form two virtual microphones (Vi and V 2 ).
  • the virtual microphones of an embodiment are directional microphones, but the embodiment is not so limited.
  • the physical microphones of an embodiment include omnidirectional microphones, but the embodiments described herein are not limited to omnidirectional microphones.
  • the virtual microphone (VM) 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.
  • PSAD VAD PSAD VAD methods
  • 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 distance di is the distance from the acoustic source to M 1
  • 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: ⁇ * 12 cm J O 1 10 on
  • 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 O 2 :
  • V 1 1 - ) - ⁇ i z) -. y . ( z ) 0, i -) - O 1 I z ) z ⁇ x
  • ⁇ (z) is a calibration filter used to compensate O 2 's response so that it is the same as Oi
  • ⁇ (z) is a filter that describes the relationship between Oi and calibrated O 2 for speech
  • ⁇ is a fixed delay that depends on the size of the array.
  • T is the temperature of the air in Celsius.
  • the filter ⁇ (z) can be calculated using wave theory to be
  • FIG. 2 is a block diagram of V 2 construction using a fixed ⁇ (z), 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. 3 is a block diagram of V 2 construction using an adaptive ⁇ (z), under an embodiment, where:
  • the adaptive process varies ⁇ (-) to minimize the output of V 2 when only speech is being received by Oi and O 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.
  • Figure 4 is a block diagram of Vi construction, under an embodiment. Now the ratio R is
  • 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 ,Sf-) 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 ⁇ iz:) in V 2 (z) for best performance.
  • 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. 5 is a flow diagram of acoustic voice activity detection 500, 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 502.
  • the detection comprises forming a filter that describes a relationship for speech between the first physical microphone and the second physical microphone 504.
  • 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 506.
  • the detection comprises generating an energy ratio of energies of the first virtual microphone and the second virtual microphone 508.
  • the detection comprises detecting acoustic voice activity of a speaker when the energy ratio is greater than a threshold value 510.
  • the accuracy of the adaptation to the ⁇ (z) of the system is a factor in determining the effectiveness of the AVAD.
  • a more accurate adaptation to the actual ⁇ (z) 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 ⁇ (z) 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 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 differences in position and angle. In practice a slightly larger amount was used (0.071 samples at 8 kHz) in order to compensate for poor calibration and diffraction effects, and this worked well.
  • the limit on the phase in the example below was implemented as the ratio of the central tap energy to the combined energy of the other taps:
  • FIG. 6 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 7 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 8 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 8) 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 under the threshold.
  • Results using the adaptive beta filter are shown in Figures 9-11.
  • 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 ⁇ 0 43 is used to filter Oi so that Oi and O 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 9 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.
  • FIG 10 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 approximately 5-30 on average, making the speech even simpler to detect using standard thresholding algorithms. There are almost no false positives or false negatives. Therefore, the response of V 2 to speech is minimal, R is very high, and all of the speech is easily detected with almost no false positives.
  • Figure 11 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
  • 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.
  • PSAD Pathfinder Speech Activity Detection
  • NAVSAD Non-Acoustic Sensor Voiced Speech Activity Detection
  • FIG. 12 is a block diagram of a NAVSAD system 1200, under an embodiment
  • the NAVSAD system couples microphones 1210 and sensors 1220 to at least one processor 1230
  • the sensors1220 of an embodiment include voicing activity detectors or non-acoustic sensors
  • the processor 1230 controls subsystems including a detection subsystem 1250, referred to herein as a detection algorithm, and a denoising subsystem 1240 Operation of the denoising subsystem 1240 is described in detail in the Related Applications
  • the NAVSAD system works extremely well in any background acoustic noise environment
  • FIG. 13 is a block diagram of a PSAD system 1300, under an embodiment
  • the PSAD system couples microphones 1210 to at least one processor 1230
  • the processor 1230 includes a detection subsystem 1250, referred to herein as a detection algorithm, and a denoising subsystem 1240
  • 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 1250 and denoising subsystems 1240 of both the NAVSAD and PSAD systems of an embodiment are algorithms controlled by the processor 1230, but are not so limited Alternative embodiments of the NAVSAD and PSAD systems can include detection subsystems 1250 and/or denoising subsystems 1240 that comprise additional hardware, firmware, software, and/or combinations of hardware, firmware, and software Furthermore, functions of the detection subsystems 1250 and denoising subsystems 1240 may be distributed across numerous components of the NAVSAD and PSAD systems
  • FIG 14 is a block diagram of a denoising subsystem 1400, 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 1400 is equivalent to the NAVSAD system 1200 when the voicing activity detector (VAD) 1420 is a non-acoustic voicing sensor 1220 and the noise removal subsystem 1440 includes the detection subsystem 1250 and the denoising subsystem 1240
  • VAD voicing activity detector
  • the noise removal subsystem 1440 includes the detection subsystem 1250 and the denoising subsystem 1240
  • the Pathfinder system 1400 is equivalent to the PSAD system 1300 in the absence of the VAD 1420, and when the noise removal subsystem 1440 includes the detection subsystem 1250 and the denoising subsystem 1240
  • 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 However, in high noise situations 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 Applications, but is not so limited Alternative embodiments, however, may use any sensor that is able to detect human tissue motion associated with speech and is unaffected by environmental acoustic noise
  • 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 Anything that moves near the sensor will cause a change in phase of the reflected wave that will be amplified and displayed as a change in voltage output from the sensor A similar sensor is described by Gregory C.
  • FIG 15 is a flow diagram of a detection algorithm 1250 for use in detecting voiced and unvoiced speech, under an embodiment
  • both the NAVSAD and PSAD systems of an embodiment include the detection algorithm 1250 as the detection subsystem 1250
  • This detection algorithm 1250 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 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 H-i 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 energy of the sensor can be determined in any one of a number of ways, and 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
  • XCORR is more susceptible to errors from acoustic noise, and in high ( ⁇ 0 dB SNR) environments is almost useless, Therefore it should not be the sole source of voicing information
  • 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 16A, 16B, and 17 show data plots for an example in which a subject twice speaks the phrase "pop pan", under an embodiment.
  • Figure 16A plots the received GEMS signal 1602 for this utterance along with the mean correlation 1604 between the GEMS signal and the Mic 1 signal and the threshold T1 used for voiced speech detection.
  • Figure 16B plots the received GEMS signal 1602 for this utterance along with the standard deviation 1606 of the GEMS signal and the threshold T2 used for voiced speech detection
  • Figure 17 plots voiced speech 1702 detected from the acoustic or audio signal 1708, along with the GEMS signal 1704 and the acoustic noise 1706; no unvoiced speech is detected in this example because of the heavy background babble noise 1706
  • 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
  • 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 m- ⁇ (n)
  • the information coming into Microphone 2 is similarly labeled ⁇ ri 2 (n)
  • the GEMS sensor is assumed available to determine voiced speech areas In the z (digital frequency) domain, these signals are represented as M 1 (Z) and M 2 (z) Then with
  • H 1 (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- ⁇ (z) can be recalculated quickly 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.
  • H 2 (z) is usually quite small, so that H 2 (z)H, (z) « 1 , and
  • the PSAD system is described 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 isotropically, 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 18 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 ⁇ d. Assuming a 1/r (or in this case 1/d) relationship, it is seen that
  • ⁇ M is the difference in gain between Mic 1 and Mic 2 and therefore Hi(z), as above in Equation 2.
  • di is the distance from Mic 1 to the speech or noise source
  • Figure 19 is a plot 1900 of ⁇ M versus di for several ⁇ d values, under an embodiment It is clear that as ⁇ d becomes larger and the noise source is closer, ⁇ M becomes larger.
  • ⁇ d 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 1900 it is clear that for small ⁇ d and for distances over approximately 30 centimeters (cm), ⁇ M is close to unity, Since most noise sources are farther away than 30 cm and are unlikely to be on the midline on the array, it is probable that when calculating H 1 (Z) as above in Equation 2, ⁇ M (or equivalents the gain of Hi(z)) will be close to unity Conversely, for noise sources that are close (within a few centimeters), there could be a substantial difference in gain depending on which microphone is closer to the noise
  • the gain will stay somewhat high during the speech portions, then descend quickly after speech ceases
  • the rapid increase and decrease in the gain of H- ⁇ (z) should be sufficient to allow the detection of speech under almost any circumstances
  • 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 20 shows a plot 2000 of the gain parameter 2002 as the sum of the absolute values of H 1 (Z) and the acoustic data 2004 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
  • 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 2000 for clarity
  • Figure 21 is an alternative plot 2100 of acoustic data presented in Figure 20
  • the data used to form plot 2000 is presented again in this plot 2100, along with audio data 2104 and GEMS data 2106 without noise to make the unvoiced speech apparent
  • a number of configurations are possible using the NAVSAD and PSAD systems to 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 While the systems described above have been described with reference to separating voiced and unvoiced speech from background acoustic noise, there are no reasons more complex classifications can not be made For more in-depth characterization of speech, the system can bandpass the information from Mic 1 and Mic 2 so that it is possible to see which bands in the Mic 1 data are more heavily composed of
  • a dual omnidirectional microphone array that provides improved noise suppression is now described.
  • the array of an embodiment is used to form two distinct virtual directional microphones, as described in detail above.
  • the two virtual microphones 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, as described in detail above, 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.
  • DOMA DOMA
  • 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).
  • DM directional microphone
  • M means a general designation for an adaptive noise suppression system microphone that usually contains more speech than noise.
  • Mic2 (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.
  • nuclel means a zero or minima in the spatial response of a physical or virtual directional microphone.
  • O 1 means a first physical omnidirectional microphone used to form a microphone array.
  • O 2 means a second physical omnidirectional microphone used to form a microphone array.
  • speech means desired speech of the user.
  • SSM Skin Surface Microphone
  • V/' 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 microphones and associated signal processing.
  • Figure 22 is a two-microphone adaptive noise suppression system 2200, under an embodiment.
  • the two-microphone system 2200 including the combination of physical microphones MIC 1 and MIC 2 along with the processing or circuitry components to which the microphones couple (described in detail below, but not shown in this figure) is referred to herein as the dual omnidirectional microphone array (DOMA) 2210, but the embodiment is not so limited.
  • the dual omnidirectional microphone array (DOMA) 2210 in analyzing the single noise source 2201 and the direct path to the microphones, the total acoustic information coming into MIC 1 (2202, which can be an physical or virtual microphone) is denoted by m ⁇ n).
  • m 2 (n) The total acoustic information coming into MIC 2 (2203, which can also be an physical or virtual microphone) is similarly labeled m 2 (n). In the z (digital frequency) domain, these are represented as Mi(z) and M 2 (z). Then,
  • M 1 (Z) S(z) + N 2 (z)
  • 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 identification algorithms and the microphone outputs when the system is certain that only noise is being received. The calculation can be done adaptively, so that the system can react to changes in the noise.
  • H 1 (Z) one of the unknowns in Equation 1.
  • H 1 (Z) the inverse of the H 1 (Z) calculation.
  • different inputs are being used (now only the speech is occurring whereas before only the noise was occurring).
  • H 2 (z) the values calculated for H 1 (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.
  • N(z) may be substituted as shown to solve for S(z) as
  • Equation 4 is much simpler to implement and is very stable, assuming H 1 (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:
  • H 1 (Z) cannot change substantially.
  • H 2 (z) cannot change substantially.
  • 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. Acoustic- only methods using information from Oi and O 2 can also return accurate VADs, but are limited to SNRs of ⁇ 3 dB or greater for adequate performance.
  • 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 23 is a generalized two-microphone array (DOMA) including an array 2301/2302 and speech source S configuration
  • Figure 24 is a system 2400 for generating or producing a first order gradient microphone V using two omnidirectional elements O 1 and O 2 , under an embodiment.
  • the array of an embodiment includes two physical microphones 2301 and 2302 (e.g., omnidirectional microphones) placed a distance 2d 0 apart and a speech source 2300 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 2301 and 2302 can be delayed (Z 1 and Z 2 ), multiplied by a gain (A 1 and A 2 ), and then summed with the other as demonstrated in Figure 24.
  • 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
  • Figure 25 is a block diagram for a DOMA 2500 including two physical microphones configured to form two virtual microphones V 1 and V 2 , under an embodiment.
  • the DOMA includes two first order gradient microphones V 1 and V 2 formed using the outputs of two microphones or elements O 1 and O 2 (2301 and 2302), under an embodiment.
  • the DOMA of an embodiment includes two physical microphones 2301 and 2302 that are omnidirectional microphones, as described above with reference to Figures 23 and 24.
  • the output from each microphone is coupled to a processing component 2502, or circuitry, and the processing component outputs signals representing or corresponding to the virtual microphones V 1 and V 2 .
  • the output of physical microphone 2301 is coupled to processing component 2502 that includes a first processing path that includes application of a first delay Z 11 and a first gain A 11 and a second processing path that includes application of a second delay Z 12 and a second gain A 12 .
  • the output of physical microphone 2302 is coupled to a third processing path of the processing component 2502 that includes application of a third delay z 21 and a third gain A 21 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 V 1
  • the output of the second and fourth processing paths is summed to form virtual microphone V 2 .
  • FIG. 26 is a block diagram for a DOMA 2600 including two physical microphones configured to form N virtual microphones V 1 through V N , where N is any number greater than one, under an embodiment.
  • the DOMA can include a processing component 2602 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 27 is an example of a headset or head-worn device 2700 that includes the DOMA, as described herein, under an embodiment.
  • the headset 2700 of an embodiment includes a housing having two areas or receptacles (not shown) that receive and hold two microphones (e.g., O 1 and O 2 ).
  • the headset 2700 is generally a device that can be worn by a speaker 2702, for example, a headset or earpiece that positions or holds the microphones in the vicinity of the speaker's mouth.
  • the headset 2700 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 O 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 22- 26).
  • 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. 28 is a flow diagram for denoising 2800 acoustic signals using the DOMA, under an embodiment.
  • the denoising 2800 begins by receiving 2802 acoustic signals at a first physical microphone and a second physical microphone. In response to the acoustic signals, a first microphone signal is output from the first physical microphone and a second microphone signal is output from the second physical microphone 2804.
  • a first virtual microphone is formed 2806 by generating a first combination of the first microphone signal and the second microphone signal.
  • a second virtual microphone is formed 2808 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 2800 generates 2810 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 29 is a flow diagram for forming 2900 the DOMA, under an embodiment.
  • Formation 2900 of the DOMA includes forming 2902 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 2904 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 combination is different from the first combination.
  • 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 V 1 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 K 2 (V) ⁇ 0, which will satisfy conditions R3 and
  • 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.
  • V 2 (z) can be represented as:
  • V 2 (z) O 2 (z)- z ⁇ O, (z)
  • the distances CJ 1 and d 2 are the distance from Oi and O 2 to the speech source (see Figure 23), 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.
  • 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 31 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.
  • V 1 (Z) can be formulated using the general form for V 1 (Z):
  • V 1 (Z) ⁇ O 1 (Z) z- dA - ⁇ B 0 2 (z) z- dB Since
  • V 2N (z) O 1N (z)-z- ⁇ -z- ⁇ ⁇ O m (z)
  • V 2N (z) (l- ⁇ )( ⁇ 1N (z)-z ⁇ )
  • ⁇ is the ratio of the distances from Oi and O 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 V 1 to speech is devoid of or includes no null and the response for speech is greater than that shown in Figure 25.
  • the linear response of virtual microphone V 1 to noise is devoid of or includes no null and the response is very similar to V 2 shown in Figure 26.
  • Figure 35 is a plot showing comparison of frequency responses for speech for the array of an embodiment and for a conventional cardioid microphone.
  • the response Of V 1 to speech is shown in Figure 32, and the response to noise in Figure 33. Note the difference in speech response compared to V 2 shown in Figure 30 and the similarity of noise response shown in Figure 31.
  • the orientation of the speech response for V 1 shown in Figure 32 is completely opposite the orientation of conventional systems, where the main lobe of response is normally oriented toward the speech source.
  • the orientation of an embodiment, in which the main lobe of the speech response Of V 1 is oriented away from the speech source means that the speech sensitivity Of V 1 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 34. This flatness of response for speech means that no shaping postfilter is needed to restore omnidirectional frequency response.
  • the speech response Of V 1 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 V 1 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 Hi(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 recommended to minimize speech distortion.
  • ⁇ 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. The effect of an incorrect ⁇ and ⁇ on the response of V 1 and V 2 can be seen by examining the definitions above :
  • V 1 (Z) O 1 (Z) - Z ⁇ - ⁇ ⁇ 0 2 (z)
  • ⁇ R and ⁇ 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 O 2 for speech into the above equations for Vi and V 2 yields
  • V ls (z) O ls (z)
  • V 2S (z) O ls (z)[ ⁇ R z ⁇ - ⁇ ⁇ z ⁇ ]
  • FIG. 36 is a plot showing speech response for V 1 (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 37 is a plot showing a ratio Of V 1 ZV 2 speech responses shown in Figure 31 versus B, under an embodiment. The ratio of MJM 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 distance and angle mismatches are included for B, then
  • ⁇ is the time difference between arrival of speech at V 1 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. Examining the temperature sensitivity, the speed of sound varies with temperature as
  • T degrees Celsius.
  • 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. For speech sources on the axis of the array, the difference in travel time for the largest change in the speed of sound is
  • 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.
  • 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 43, 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.
  • the description above has assumed that the microphones Oi and O 2 were calibrated so that their response to a source located the same distance away was identical for both amplitude and phase. This is not always feasible, so a more practical calibration procedure is presented below. It is not as accurate, but is much simpler to implement.
  • ⁇ (z) such that:
  • V 1 (Z) O 1 (Z) z-* - ⁇ (z) ⁇ (z)0 2 (z)
  • V 2 (z) ⁇ (z)0 2 (z)- Z - ⁇ ⁇ (z)0 1 (z)
  • 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 ⁇ (z) to remove as much speech as possible. This is accomplished by:
  • a simple adaptive filter can be used for ⁇ (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 1 (Z) O 1 (Z) z ⁇ ' - B, ⁇ ⁇ O 2 (z)
  • V 2 (z) O 2 (z) - z- ⁇ ⁇ B 2 ⁇ ⁇ O, (z)
  • This formulation also allows the virtual microphone responses to be varied but retains the all-pass characteristic of Hi(z).
  • the system is flexible enough to operate well at a variety of Bl values, but B2 values should be close to unity to limit devoicing for best performance.
  • Embodiments described herein include a method comprising: forming a first virtual microphone by combining a first signal of a first physical microphone and a second signal of a second physical microphone; forming a filter that describes a relationship for speech between the first physical microphone and the second physical microphone; 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; generating an energy ratio of energies of the first virtual microphone and the second virtual microphone; and detecting acoustic voice activity of a speaker when the energy ratio is greater than a threshold value.
  • the first virtual microphone and the second virtual microphone of an embodiment are distinct virtual directional microphones.
  • the first virtual microphone and the second virtual microphone of an embodiment have approximately similar responses to noise.
  • the first virtual microphone and the second virtual microphone of an embodiment have approximately dissimilar responses to speech.
  • the method of an embodiment comprises applying a calibration to at least one of the first signal and the second signal.
  • the calibration of an embodiment compensates a second response of the second physical microphone so that the second response is equivalent to a first response of the first physical microphone.
  • the method of an embodiment comprises applying a delay to the first intermediate signal.
  • the delay of an embodiment is proportional to a time difference between arrival of the speech at the second physical microphone and arrival of the speech at the first physical microphone.
  • the forming of the first virtual microphone of an embodiment comprises applying the filter to the second signal.
  • the forming of the first virtual microphone of an embodiment comprises applying the calibration to the second signal.
  • the forming of the first virtual microphone of an embodiment comprises applying the delay to the first signal.
  • the forming of the first virtual microphone by the combining of an embodiment comprises subtracting the second signal from the first signal.
  • the filter of an embodiment is an adaptive filter.
  • the method of an embodiment comprises adapting the filter to minimize a second virtual microphone output when only speech is being received by the first physical microphone and the second physical microphone.
  • the adapting of an embodiment comprises applying a least-mean squares process.
  • the method of an embodiment comprises generating coefficients of the filter during a period when only speech is being received by the first physical microphone and the second physical microphone.
  • the forming of the filter of an embodiment comprises generating a first quantity by applying a calibration to the second signal .
  • the forming of the filter of an embodiment comprises generating a second quantity by applying the delay to the first signal.
  • the forming of the filter of an embodiment comprises forming the filter as a ratio of the first quantity to the second quantity.
  • the generating of the energy ratio of an embodiment comprises generating the energy ratio for a frequency band.
  • the generating of the energy ratio of an embodiment comprises generating the energy ratio for a frequency subband.
  • the frequency subband of an embodiment includes frequencies higher than approximately 200 Hertz (Hz).
  • the frequency subband of an embodiment includes frequencies in a range from approximately 250 Hz to 1250 Hz.
  • the frequency subband of an embodiment includes frequencies in a range from approximately 200 Hz to 3000 Hz.
  • the filter of an embodiment is a static filter.
  • the forming of the filter of an embodiment comprises determining a first distance as distance between the first physical microphone and a mouth of the speaker.
  • the forming of the filter of an embodiment comprises determining a second distance as distance between the second physical microphone and the mouth.
  • the forming of the filter of an embodiment comprises forming a ratio of the first distance to the second distance.
  • the method of an embodiment comprises generating a vector of the energy ratio versus time.
  • the first and second physical microphones of an embodiment are omnidirectional microphones.
  • the method of an embodiment comprises positioning the first physical microphone and the second physical microphone along an axis and separating the first physical microphone and the second physical microphone by a first distance.
  • a midpoint of the axis of an embodiment is a second distance from a mouth of the speaker, wherein the mouth is located in a direction defined by an angle relative to the midpoint.
  • Embodiments described herein include a method comprising: forming a first virtual microphone; forming a filter by generating a first quantity by applying a calibration to a second signal of a second physical microphone, generating a second quantity by applying the delay to a first signal of a first physical microphone, and forming the filter as a ratio of the first quantity to the second quantity; 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; and generating a ratio of energies of the first virtual microphone and the second virtual microphone and detecting acoustic voice activity using the ratio.
  • the first virtual microphone and the second virtual microphone of an embodiment have approximately similar responses to noise and approximately dissimilar responses to speech.
  • the method of an embodiment comprises applying a calibration to at least one of the first signal and the second signal, wherein the calibration compensates a second response of the second physical microphone so that the second response is equivalent to a first response of the first physical microphone.
  • the method of an embodiment comprises applying a delay to the first intermediate signal, wherein the delay is proportional to a time difference between arrival of the speech at the second physical microphone and arrival of the speech at the first physical microphone.
  • the forming of the first virtual microphone of an embodiment comprises applying the filter to the second signal.
  • the forming of the first virtual microphone of an embodiment comprises applying the calibration to the second signal.
  • the forming of the first virtual microphone of an embodiment comprises applying the delay to the first signal.
  • the forming of the first virtual microphone by the combining of an embodiment comprises subtracting the second signal from the first signal.
  • the filter of an embodiment is an adaptive filter.
  • the method of an embodiment comprises adapting the filter to minimize a second virtual microphone output when only speech is being received by the first physical microphone and the second physical microphone.
  • the adapting of an embodiment comprises applying a least-mean squares process.
  • the method of an embodiment comprises generating coefficients of the filter during a period when only speech is being received by the first physical microphone and the second physical microphone.
  • the generating of the ratio of an embodiment comprises generating the ratio for a frequency band.
  • the generating of the ratio of an embodiment comprises generating the ratio for a frequency subband.
  • the method of an embodiment comprises generating a vector of the ratio versus time.
  • Embodiments described herein include a method comprising : forming a first virtual microphone by generating a first combination of a first signal and a second signal, wherein the first signal is received from a first physical microphone and the second signal is received from a second physical microphone; forming a filter by generating a first quantity by applying a calibration to at least one of the first signal and the second signal, generating a second quantity by applying a delay to the first signal, and forming the filter as a ratio of the first quantity to the second quantity; and 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; and determining a presence of acoustic voice activity of a speaker when an energy ratio of energies of the first virtual microphone and the second virtual microphone is greater than a threshold value.
  • Embodiments described herein include an acoustic voice activity detection system comprising : a first virtual microphone comprising a first combination of a first signal and a second signal, wherein the first signal is received from a first physical microphone and the second signal is received from a second physical microphone; a filter, wherein the filter is formed by generating a first quantity by applying a calibration to at least one of the first signal and the second signal, generating a second quantity by applying a delay to the first signal, and forming the filter as a ratio of the first quantity to the second quantity; and a second virtual microphone formed by applying the filter to the first signal to generate a first intermediate signal and summing the first intermediate signal and the second signal, wherein acoustic voice activity of a speaker is determined to be present when an energy ratio of energies of the first virtual microphone and the second virtual microphone is greater than a threshold value.
  • the first virtual microphone and the second virtual microphone of an embodiment have approximately similar responses to noise and approximately dissimilar responses to speech.
  • a calibration is applied to the second signal of an embodiment, wherein the calibration compensates a second response of the second physical microphone so that the second response is equivalent to a first response of the first physical microphone.
  • the delay is applied to the first intermediate signal of an embodiment, wherein the delay is proportional to a time difference between arrival of the speech at the second physical microphone and arrival of the speech at the first physical microphone.
  • the first virtual microphone of an embodiment is formed by applying the filter to the second signal.
  • the first virtual microphone of an embodiment is formed by applying the calibration to the second signal.
  • the first virtual microphone of an embodiment is formed by applying the delay to the first signal.
  • the first virtual microphone of an embodiment is formed by subtracting the second signal from the first signal.
  • the filter of an embodiment is an adaptive filter.
  • the filter of an embodiment is adapted to minimize a second virtual microphone output when only speech is being received by the first physical microphone and the second physical microphone.
  • Coefficients of the filter of an embodiment are generated during a period when only speech is being received by the first physical microphone and the second physical microphone.
  • the energy ratio of an embodiment comprises an energy ratio for a frequency band.
  • the energy ratio of an embodiment comprises an energy ratio for a frequency subband .
  • Embodiments described herein include a device comprising : a first physical microphone generating a first signal; a second physical microphone generating a second signal; and a processing component coupled to the first physical microphone and the second physical microphone, the processing component forming a first virtual microphone, the processing component forming a filter that describes a relationship for speech between the first physical microphone and the second physical microphone, the processing component 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, the processing component detecting acoustic voice activity of a speaker when an energy ratio of energies of the first virtual microphone and the second virtual microphone is greater than a threshold value.
  • the device of an embodiment comprises applying a calibration to at least one of the first signal and the second signal.
  • the calibration of an embodiment compensates a second response of the second physical microphone so that the second response is equivalent to a first response of the first physical microphone.
  • the device of an embodiment comprises applying a delay to the first intermediate signal.
  • the delay of an embodiment is proportional to a time difference between arrival of the speech at the second physical microphone and arrival of the speech at the first physical microphone.
  • the forming of the first virtual microphone of an embodiment comprises applying the filter to the second signal.
  • the forming of the first virtual microphone of an embodiment comprises applying the calibration to the second signal.
  • the forming of the first virtual microphone of an embodiment comprises applying the delay to the first signal.
  • the forming of the first virtual microphone by the combining of an embodiment comprises subtracting the second signal from the first signal.
  • the filter of an embodiment is an adaptive filter.
  • the device of an embodiment comprises adapting the filter to minimize a second virtual microphone output when only speech is being received by the first physical microphone and the second physical microphone.
  • the adapting of an embodiment comprises applying a least-mean squares process.
  • the device of an embodiment comprises generating coefficients of the filter during a period when only speech is being received by the first physical microphone and the second physical microphone.
  • the forming of the filter of an embodiment comprises generating a first quantity by applying a calibration to the second signal.
  • the forming of the filter of an embodiment comprises generating a second quantity by applying the delay to the first signal.
  • the forming of the filter of an embodiment comprises forming the filter as a ratio of the first quantity to the second quantity.
  • the generating of the energy ratio of an embodiment comprises generating the energy ratio for a frequency band.
  • the generating of the energy ratio of an embodiment comprises generating the energy ratio for a frequency subband.
  • the frequency subband of an embodiment includes frequencies higher than approximately 200 Hertz (Hz).
  • the frequency subband of an embodiment includes frequencies in a range from approximately 250 Hz to 1250 Hz.
  • the frequency subband of an embodiment includes frequencies in a range from approximately 200 Hz to 3000 Hz.
  • the filter of an embodiment is a static filter.
  • the forming of the filter of an embodiment comprises determining a first distance as distance between the first physical microphone and a mouth of the speaker.
  • the forming of the filter of an embodiment comprises determining a second distance as distance between the second physical microphone and the mouth.
  • the forming of the filter of an embodiment comprises forming a ratio of the first distance to the second distance.
  • the device of an embodiment comprises generating a vector of the energy ratio versus time.
  • the first virtual microphone and the second virtual microphone of an embodiment are distinct virtual directional microphones.
  • the first virtual microphone and the second virtual microphone of an embodiment have approximately similar responses to noise.
  • the first virtual microphone and the second virtual microphone of an embodiment have approximately dissimilar responses to speech.
  • the first and second physical microphones of an embodiment are omnidirectional microphones.
  • the device of an embodiment comprises positioning the first physical microphone and the second physical microphone along an axis and separating the first physical microphone and the second physical microphone by a first distance.
  • a midpoint of the axis of an embodiment is a second distance from a mouth of the speaker, wherein the mouth is located in a direction defined by an angle relative to the midpoint.
  • Embodiments described herein include a device comprising : a headset including at least one loudspeaker, wherein the headset attaches to a region of a human head; a microphone array connected to the headset, the microphone array including a first physical microphone outputting a first signal and a second physical microphone outputting a second signal; and a processing component coupled to the first physical microphone and the second physical microphone, the processing component forming a first virtual microphone, the processing component forming a filter that describes a relationship for speech between the first physical microphone and the second physical microphone, the processing component 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, the processing component detecting acoustic voice activity of a speaker when an energy ratio of energies of the first virtual microphone and the second virtual microphone is greater than a threshold value.
  • the AVAD can be a component of a single system, multiple systems, and/or geographically separate systems.
  • the AVAD can also be a subcomponent or subsystem of a single system, multiple systems, and/or geographically separate systems.
  • the AVAD can be coupled to one or more other components (not shown) of a host system or a system coupled to the host system.
  • One or more components of the AVAD and/or a corresponding system or application to which the AVAD is coupled or connected includes and/or runs 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.
  • aspects of the AVAD and corresponding systems and methods described herein may be implemented as functionality programmed into any of a variety of circuitry, including programmable logic devices (PLDs), such as field programmable gate arrays (FPGAs), programmable array logic (PAL) devices, electrically programmable logic and memory devices and standard cell-based devices, as well as application specific integrated circuits (ASICs).
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • PAL programmable array logic
  • ASICs application specific integrated circuits
  • microcontrollers with memory such as electronically erasable programmable read only memory (EEPROM)
  • embedded microprocessors firmware, software, etc.
  • aspects of the AVAD and corresponding systems and methods may be embodied in microprocessors having software- based circuit emulation, discrete logic (sequential and combinatorial), custom devices, fuzzy (neural) logic, quantum devices, and hybrids of any of the above device types.
  • the underlying device technologies may be provided in a variety of component types, e.g., metal-oxide semiconductor field-effect transistor (MOSFET) technologies like complementary metal-oxide semiconductor (CMOS), bipolar technologies like emitter-coupled logic (ECL), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer- metal structures), mixed analog and digital, etc.
  • MOSFET metal-oxide semiconductor field-effect transistor
  • CMOS complementary metal-oxide semiconductor
  • ECL emitter-coupled logic
  • polymer technologies e.g., silicon-conjugated polymer and metal-conjugated polymer- metal structures
  • mixed analog and digital etc.
  • any system, method, and/or other components disclosed herein may be described using computer aided design tools and expressed (or represented), as data and/or instructions embodied in various computer-readable media, in terms of their behavioral, register transfer, logic component, transistor, layout geometries, and/or other characteristics.
  • Computer-readable media in which such formatted data and/or instructions may be embodied include, but are not limited to, non-volatile storage media in various forms (e.g., optical, magnetic or semiconductor storage media) and carrier waves that may be used to transfer such formatted data and/or instructions through wireless, optical, or wired signaling media or any combination thereof.
  • Examples of transfers of such formatted data and/or instructions by carrier waves include, but are not limited to, transfers (uploads, downloads, e-mail, etc.) over the Internet and/or other computer networks via one or more data transfer protocols (e.g., HTTP, FTP, SMTP, etc.).
  • data transfer protocols e.g., HTTP, FTP, SMTP, etc.
  • a processing entity e.g., one or more processors
  • processors within the computer system in conjunction with execution of one or more other computer programs.
  • the terms used should not be construed to limit the AVAD and corresponding systems and methods to the specific embodiments disclosed in the specification and the claims, but should be construed to include all systems that operate under the claims. Accordingly, the AVAD and corresponding systems and methods is not limited by the disclosure, but instead the scope is to be determined entirely by the claims. While certain aspects of the AVAD and corresponding systems and methods are presented below in certain claim forms, the inventors contemplate the various aspects of the AVAD and corresponding systems and methods in any number of claim forms. Accordingly, the inventors reserve the right to add additional claims after filing the application to pursue such additional claim forms for other aspects of the AVAD and corresponding systems and methods.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Circuit For Audible Band Transducer (AREA)

Abstract

L’invention a pour objet des procédés et des systèmes de détection acoustique d’activité vocale (AVAD). Ces procédés et ces systèmes AVAD, notamment les algorithmes ou programmes correspondants, utilisent des microphones pour générer des microphones directionnels virtuels qui présentent des réponses de bruit très semblables et des réponses de paroles très différentes. Le rapport des énergies des microphones virtuels est ensuite calculé sur une taille de fenêtre donnée et le rapport peut alors être utilisé avec une variété de procédés pour générer un signal VAD. Les microphones virtuels peuvent être construits en utilisant un filtre adaptatif ou un filtre fixe.
PCT/US2009/062129 2008-10-24 2009-10-26 Détection acoustique d’activité vocale (avad) pour systèmes électroniques WO2010048635A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
CA2741652A CA2741652A1 (fr) 2008-10-24 2009-10-26 Detection acoustique d'activite vocale (avad) pour systemes electroniques
AU2009308442A AU2009308442A1 (en) 2008-10-24 2009-10-26 Acoustic Voice Activity Detection (AVAD) for electronic systems
CN2009801515125A CN102282865A (zh) 2008-10-24 2009-10-26 用于电子系统的声学语音活动检测(avad)
EP09822855.4A EP2353302A4 (fr) 2008-10-24 2009-10-26 Détection acoustique d activité vocale (avad) pour systèmes électroniques

Applications Claiming Priority (2)

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US10842608P 2008-10-24 2008-10-24
US61/108,426 2008-10-24

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WO2010048635A1 true WO2010048635A1 (fr) 2010-04-29

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CN (1) CN102282865A (fr)
AU (2) AU2009308442A1 (fr)
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WO (1) WO2010048635A1 (fr)

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US9196261B2 (en) 2000-07-19 2015-11-24 Aliphcom Voice activity detector (VAD)—based multiple-microphone acoustic noise suppression
US9066186B2 (en) 2003-01-30 2015-06-23 Aliphcom Light-based detection for acoustic applications
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CN102282865A (zh) 2011-12-14
AU2016202314A1 (en) 2016-05-05
EP2353302A1 (fr) 2011-08-10
CA2741652A1 (fr) 2010-04-29
AU2009308442A1 (en) 2010-04-29

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