WO2007014136A9 - Robust separation of speech signals in a noisy environment - Google Patents
Robust separation of speech signals in a noisy environmentInfo
- Publication number
- WO2007014136A9 WO2007014136A9 PCT/US2006/028627 US2006028627W WO2007014136A9 WO 2007014136 A9 WO2007014136 A9 WO 2007014136A9 US 2006028627 W US2006028627 W US 2006028627W WO 2007014136 A9 WO2007014136 A9 WO 2007014136A9
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- signal
- speech
- noise
- voice activity
- microphone
- Prior art date
Links
Classifications
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/20—Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech
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- 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
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- 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
- G10L21/0272—Voice signal separating
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- 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
- G10L25/84—Detection of presence or absence of voice signals for discriminating voice from noise
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- 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
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- 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 present invention relates to processes and methods for separating a speech signal from a noisy acoustic environment. More particularly, one example of the present invention provides a blind signal source process for separating a speech signal from a noisy environment.
- An acoustic environment is often noisy, making it difficult to reliably detect and react to a desired informational signal.
- a person may desire to communicate with another person using a voice communication channel.
- the channel may be provided, for example, by a mobile wireless handset, a walkie-talkie, a two-way radio, or other communication device.
- the person may use a headset or earpiece connected to the communication device.
- the headset or earpiece often has one or more ear speakers and a microphone.
- the microphone extends on a boom toward the person's mouth, to increase the likelihood that the microphone will pick up the sound of the person speaking.
- the microphone receives the person's voice signal, and converts it to an electronic signal.
- the microphone also receives sound signals from various noise sources, and therefore also includes a noise component in the electronic signal. Since the headset may position the microphone several inches from the person's mouth, and the environment may have many uncontrollable noise sources, the resulting electronic signal may have a substantial noise component. Such substantial noise causes an unsatisfactory communication experience, and may cause the communication device to operate in an inefficient manner, thereby increasing battery drain.
- a speech signal is generated in a noisy environment, and speech processing methods are used to separate the speech signal from the environmental noise.
- speech signal processing is important in many areas of everyday communication, since noise is almost always present in real-world conditions.
- Noise is defined as the combination of all signals interfering or degrading the speech signal of interest.
- the real world abounds from multiple noise sources, including single point noise sources, which often transgress into multiple sounds resulting in reverberation. Unless separated and isolated from background noise, it is difficult to make reliable and efficient use of the desired speech signal.
- Background noise may include numerous noise signals generated by the general environment, signals generated by background conversations of other people, as well as reflections and reverberation generated from each of the signals.
- Speech communication mediums such as cell phones, speakerphones, headsets, cordless telephones, teleconferences, CB radios, walkie-talkies, computer telephony applications, computer and automobile voice command applications and other hands-free applications, intercoms, microphone systems and so forth, can take advantage of speech signal processing to separate the desired speech signals from background noise.
- BSS blind source separation
- each of the source signals is delayed and attenuated in some time varying manner during transmission from source to microphone, where it is then mixed with other independently delayed and attenuated source signals, including multipath versions of itself (reverberation), which are delayed versions arriving from different directions.
- a person receiving all these acoustic signals may be able to listen to a particular set of sound source while filtering out or ignoring other interfering sources, including multi-path signals.
- a first module uses direction-of -arrival information to extract the original source signals while any residual crosstalk between the channels is removed by a second module.
- Such an arrangement may be effective in separating spatially localized point sources with clearly defined direction-of- arrival but fails to separate out a speech signal in a real-world spatially distributed noise environment for which no particular direction-of-arrival can be determined.
- ICA Independent Component Analysis
- independent component analysis operates an "un-mixing" matrix of weights on the mixed signals, for example multiplying the matrix with the mixed signals, to produce separated signals.
- the weights are assigned initial values, and then adjusted to maximize joint entropy of the signals in order to minimize information redundancy.
- This weight-adjusting and entropy- IrIr 1 T 1 OaCm(T nror ess is repeated until the information redundancy of the signals is inimum. Because this technique does not require information on the source of each signal, it is known as a "blind source separation" method. Blind separation problems refer to the idea of separating mixed signals that come from multiple independent sources.
- ICA algorithms are not able to effectively separate signals that have been recorded in a real environment which inherently include acoustic echoes, such as those due to room architecture related reflections. It is emphasized that the methods mentioned so far are restricted to the separation of signals resulting from a linear stationary mixture of source signals. The phenomenon resulting from the summing of direct path signals and their echoic counterparts is termed reverberation and poses a major issue in artificial speech enhancement and recognition systems. ICA algorithms may require long filters which can separate those time-delayed and echoed signals, thus precluding effective real time use.
- Known ICA signal separation systems typically use a network of filters, acting as a neural network, to resolve individual signals from any number of mixed signals input into the filter network. That is, the ICA network is used to separate a set of sound signals into a more ordered set of signals, where each ⁇ nts a particular sound source. For example, if an ICA network receives a sound signal comprising piano music and a person speaking, a two port ICA network will separate the sound into two signals: one signal having mostly piano music, and another signal having mostly speech.
- Another prior technique is to separate sound based on auditory scene analysis.
- auditory scene analysis In this analysis, vigorous use is made of assumptions regarding the nature of the sources present. It is assumed that a sound can be decomposed into small elements such as tones and bursts, which in turn can be grouped according to attributes such as harmonicity and continuity in time. Auditory scene analysis can be performed using information from a single microphone or from several microphones. The field of auditory scene analysis has gained more attention due to the availability of computational machine learning approaches leading to computational auditory scene analysis or CASA. Although interesting scientifically since it involves the understanding of the human auditory processing, the model assumptions and the computational techniques are still in its infancy to solve a realistic cocktail party scenario.
- a widely known technique for linear microphone-array processing is often referred to as "beamforming".
- the time difference between signals due to spatial difference of microphones is used to enhance the signal. More particularly, it is likely that one of the microphones will "look" more directly at the speech source, whereas the other microphone may generate a signal that is relatively attenuated. Although some attenuation can be achieved, the beamformer cannot provide relative attenuation of frequency components whose wavelengths are larger than the array.
- Beamforming techniques make no assumption on the sound source but assume that the geometry between source and sensors or the sound signal itself is known for the purpose of dereverberating the signal or localizing the sound source.
- GSC Generalized Sidelobe Canceling
- GSC aims at filtering out a single desired source signal z_i from a set of measurements x, as more fully explained inThe GSC principle / Griffiths, L.J., Jim, CW., An alternative approach to linear constrained adaptive beamforming, IEEE Transaction Antennas and Propagation, vol 30, no 1, pp.27-34, Jan 1982.
- GSC predefines that a signal-independent beamformer c filters the sensor signals so that the direct path from the desired source remains undistorted whereas, ideally, other directions should be suppressed. Most often, the position of the desired source must be pre-determined by additional localization methods.
- an adaptive blocking matrix B aims at suppressing all components originating from the desired signal z_i so that only noise components appear at the output of B. From these, an adaptive interference canceller a derives an estimate for the remaining noise component in the output of c, by minimizing an estimate of the total output power E(z_i*z_i).
- the fixed beamformer c and the interference canceller a jointly perform interference suppression. Since GSC requires the desired speaker to be confined to a limited tracking region, its applicability is limited to spatially rigid scenarios.
- Another known technique is a class of active-cancellation algorithms, which is related to sound separation.
- this technique requires a "reference signal,” i.e., a signal derived from only of one of the sources.
- Active noise-cancellation and echo cancellation techniques make extensive use of this technique and the noise reduction is relative to the contribution of noise to a mixture by filtering a known signal that contains only the noise, and subtracting it from the mixture. This method assumes that one of the measured signals consists of one and only one source, an assumption which is not realistic in many real life settings.
- blind Techniques for active cancellation that do not require a reference signal are called “blind” and are of primary interest in this application. They are now classified, based on the degree of realism of the underlying assumptions regarding the acoustic processes by which the unwanted signals reach the microphones.
- One class of blind active-cancellation techniques may be called “gain-based” or also known as “instantaneous mixing”: it is presumed . that the waveform produced by each source is received by the microphones simultaneously, but with varying relative gains. (Directional microphones are most often used to produce the required differences in gain.)
- a gain-based system attempts to cancel copies of an undesired source in different microphone signals by applying relative gains to the microphone signals and subtracting, but not applying time delays or other filtering.
- the parameter m is the number of sources
- L is the convolution order and depends on the environment acoustics and t indicates the time index.
- the first summation is due to filtering of the sources in the environment and the second summation is due to the mixing of the different sources.
- Most of the work on ICA has been centered on algorithms for instantaneous mixing scenarios in which the first summation is removed and the task is to simplified to inverting a mixing matrix a.
- a slight modification is when assuming no reverberation, signals originating from point sources can be viewed as identical when recorded at different microphone locations except for an amplitude factor and a delay.
- the problem as described in the above equation is known as the multichannel blind deconvolution problem.
- a signal separation process is associated with a voice activity detector.
- the voice activity detector is a two-channel detector, which enables a particularly robust and accurate detection of voice activity.
- the control signal is used to activate, adjust, or control signal separation processes or post-processing operations to improve the quality of the resulting speech signal.
- a signal separation process is provided as a learning stage and an output stage. The learning stage aggressively adjusts to current acoustic conditions, and passes coefficients to the output stage. The output stage adapts more slowly, and generates a speech-content signal and a noise dominant signal. Should the learning stage becomes unstable, only the learning stage is reset, allowing the output stage to continue outputting a high quality speech signal.
- a separation process receives two input signals generated by respective microphones.
- the microphones have a predetermined relationship with the target speaker, so one microphone generates a speech-dominant signal, while the other microphone generates a noise-dominant signal.
- Both signals are received into a signal separation process, and the outputs from the signal separation process are further processed in a set of post-processing operations.
- a scaling monitor monitors the signal separation process or one or more of the post processing operations. To make an adjustment in the signal separation process, the scaling monitor may control the scaling or amplification of the input signals.
- each input signal may be scaled independently. By scaling one or both of the input signals, the signal separation process may be made to operate more effectively or aggressively, allowing for less post processing, and enhancing overall speech signal quality.
- the signals from the microphones are monitored for the occurrence of wind noise.
- wind noise is detected from one microphone, that microphone is deactivated or de-emphasized, and the system is set to operate as a single channel system.
- the microphone is reactivated and the system returns to normal two channel operation.
- FIG. 1 is a block diagram of a process for separating a speech signal in accordance with the present invention
- FIG. 2 is a block diagram of a process for separating a speech signal in accordance with the present invention
- FIG. 3 is a block diagram of a voice detection process in accordance with the present invention.
- FIG. 4 is a block diagram of a voice detection process in accordance with the present invention.
- FIG. 5 is a block diagram of a process for separating a speech signal in accordance with the present invention
- FIG. 6 is a block diagram of a process for separating a speech signal in accordance with the present invention.
- FIG. 7 is a block diagram of a process for separating a speech signal in accordance with the present invention.
- FIG. 8 is a is a diagram of a wireless earpiece in accordance with the present invention.
- FIG. 9 is a flowchart of a separation process in accordance with the present invention.
- FIG. 10 is a block diagram of one embodiment of an improved ICA processing sub-module in accordance with the present invention.
- FIG. 11 is a block diagram of one embodiment of an improved ICA speech separation process in accordance with the present invention.
- FIG. 12 is a block diagram of a process for resetting a signal separation process in accordance with the present invention.
- FIG. 13 is a block diagram of a process for scaling the input signals to a signal separation process in accordance with the present invention.
- FIG. 14 is a flowchart of a process for managing wind noise in accordance with the present invention.
- Speech separation process 100 has a set of signal inputs (e.g., sound signals from microphones) 102 and 104 that have a predefined relationship with an expected speaker.
- signal input 102 may be from a microphone arranged to be closest to the speaker's mouth, while signal input 104 may be from a microphone spaced farther away from the speaker's mouth.
- the speech separation process 106 generally has two separate but interrelated processes.
- the separation process 106 has a signal separation process 108, which may be, for example, a blind signal source (BSS) or independent component analysis (ICA) process.
- BSS blind signal source
- ICA independent component analysis
- the microphones generate a pair of input signals to the signal separation process 108, and the signal separation process generates a signal having speech content 112, and a noise-dominant signal 114.
- the post processing steps 110 accept these signals, and further reduce the noise to generate an output speech signal 121, which may be transmitted 125 by transmission subsystem 123.
- process 100 uses a voice activity detector 106 to activate, adjust, or control selected signal separation, post processing, or transmission functions.
- the voice activity detector is a two channel detector, enabling the voice activity detector ("VAD") to operate in a particularly robust and accurate fashion.
- VAD voice activity detector
- the VAD 106 receives two input signals 105, with one of the signals defined to hold a stronger speech signal.
- the VAD has a simple and efficient way to determine when speech is present.
- the VAD 106 Upon detecting speech, the VAD 106 generates a control signal 107.
- the control signal may be used, for example, to activate the signal separation process only when speech is occurring, thereby increasing stability and saving power.
- the post processing steps 110 may be controlled to more accurately characterize noise, as the characterization process may be limited to times when no speech is occurring. With a better characterization of noise, remnants of the noise signal may be more effectively removed from the speech signal. As will be further described below, the robust and accurate VAD 106 enables a more stable and effective speech separation process.
- Communication process 175 has a first microphone 177 generating a first microphone signal 178 that is received into the speech separation process 180.
- Second microphone 175 generates a second microphone signal 182 which is also received into speech separation process 180.
- the voice activity detector 185 receives first microphone signal 178 and second microphone signal 182. It will be appreciated that the microphone signals may be filtered, digitized, or otherwise processed.
- the first microphone 177 is positioned closer to the speaker's mouth then microphone 179. This predefined arrangement enables simplified identification of the speech signal, as well as improved voice activity detection.
- the two channel voice activity detector 185 may operate a process similar to the process described with reference to figure 3 or figure 4.
- voice activity detector 185 is a two channel voice activity detector, as described with reference to figures 3 or 4. This means that VAD 185 is particularly robust and accurate for reasonable SNRs, and therefore may confidently be used as a core control mechanism in the communication process 175. When the two channel voice activity detector 185 detects speech, it generates control signal 186.
- Control signal 186 may be advantageously used to activate, control, or adjust several processes in communication process 175.
- speech separation process 180 may be adaptive and learn according to the specific acoustic environment. Speech separation process 180 may also adapt to particular microphone placement, the acoustic environment, or a particular user's speech.
- the learning process 188 may be activated responsive to the voice activity control signal 186. In this way, the speech separation process only applies its adaptive learning processes when desired speech is likely occurring. Also, by deactivating the learning processing when only noise is present, or alternatively, absent, processing and battery power may be conserved.
- the speech separation process will be described as an independent component analysis (ICA) process.
- ICA independent component analysis
- the ICA module is not able to perform its main separation function in any time interval when the desired speaker is not speaking, and therefore may be turned off.
- This "on” and “off” state can be monitored and controlled by the voice activity detection module 185 based on comparing energy content between input channels or desired speaker a priori knowledge such as specific spectral signatures.
- the ICA filters do not inappropriately adapt, thereby enabling adaptation only when such adaptation will be able to achieve a separation improvement.
- Controlling adaptation of ICA filters allows the ICA process to achieve and maintain good separation quality even after prolonged periods of desired speaker silence and avoid algorithm singularities due to unfruitful separation efforts for addressing situations the ICA stage cannot solve.
- Various ICA algorithms exhibit different degrees of robustness or stability towards isotropic noise but turning off the ICA stage during desired speaker absence, or alternatively noise absence, adds significant robustness to the methodology. Also, by deactivating the ICA processing when only noise is present, processing and battery power may be conserved.
- IIR filtering itself can result in non bounded outputs due to accumulation of past filter errors (numeric instability)
- techniques used in finite precision coding to check for instabilities can be used.
- the explicit evaluation of input and output energy to the ICA filtering stage is used to detect anomalies and reset the filters and filtering history to values provided by the supervisory module.
- the voice activity detector control signal 186 is used to set a volume adjustment 189.
- volume on speech signal 181 may be substantially reduced at times when no voice activity is detected. Then, when voice activity is detected, the volume may be increased on speech signal 181. This volume adjustment may also be made on the output of any post processing stage. This not only provides for a better communication signal, but also saves limited battery power.
- noise estimation processes 190 may be used to determine when noise reduction processes may be more aggressively operated when no voice activity is detected. Since the noise estimation process 190 is now aware of when a signal is only noise, it may more accurately characterize the noise signal.
- noise processes can be better adjusted to the actual noise characteristics, and may be more aggressively applied in periods with no speech. Then, when voice activity is detected, the noise reduction processes may be adjusted to have a less degrading effect on the speech signal.
- some noise reduction processes are known to create undesirable artifacts in speech signal, although they are may be highly effective in reducing noise. These noise processes may be operated when no speech signal is present, but may be disabled or adjusted when speech is likely present.
- the control signal 186 may be used to adjust certain noise reduction processes 192.
- noise reduction process 192 may be a spectral subtraction process. More particularly, signal separation process 180 generates a noise signal 196 and a speech signal 181. The speech signal 181 may have still have a noise component, and since the noise signal 196 accurately characterizes the noise, the spectral subtraction process 192 may be used to further remove noise from the speech signal. However, such a spectral subtraction also acts to reduce the energy level of the remaining speech signal. Accordingly, when the control signal indicates that speech is present, the noise reduction process may be adjusted to compensate for the spectral subtraction by applying a relatively small amplification to the remaining speech signal. This small level of amplification results in a more natural and consistent speech signal. Also, since the noise reduction process 190 is aware of how aggressively the spectral subtraction was performed, the level of amplification can be accordingly adjusted.
- the control signal 186 may also be used to control the automatic gain control (AGC) function 194.
- AGC automatic gain control
- the AGC is applied to the output of the speech signal 181, and is used to maintain the speech signal in a usable energy level. Since the AGC is aware of when speech is present, the AGC can more accurately apply gain control to the speech signal. By more accurately controlling or normalizing the output speech signal, post processing functions may be more easily and effectively applied. Also, the risk of saturation in post processing and transmission is reduced. It will be understood that the control signal 186 may be advantageously used to control or adjust several processes in the communication system, including other post processing 195 functions.
- the AGC can be either fully adaptive or have a fixed gain.
- the AGC supports a fully adaptive operating mode with a range of about -30 dB to 30 dB.
- a default gain value may be independently established, and is typically 0 dB. If adaptive gain control is used, the initial gain value is specified by this default gain.
- the AGC adjusts the gain factor in accordance with the power level of an input signal 181. Input signals 181 with a low energy level are amplified to a comfortable sound level, while high energy signals are attenuated.
- a multiplier applies a gain factor to an input signal which is then output.
- the default gain typically 0 dB is initially applied to the input signal.
- a power estimator estimates the short term average power of the gain adjusted signal.
- the short term average power of the input signal is preferably calculated every eight samples, typically every one ms for a 8 kHz signal.
- Clipping logic analyzes the short term average power to identify gain adjusted signals whose amplitudes are greater than a predetermined clipping threshold.
- the clipping logic controls an AGC bypass switch, which directly connects the input signal to the media queue when the amplitude of the gain adjusted signal exceeds the predetermined clipping threshold.
- the AGC bypass switch remains in the up or bypass position until the AGC adapts so that the amplitude of the gain adjusted signal falls below the clipping threshold.
- the AGC is designed to adapt slowly, although it should adapt fairly quickly if overflow or clipping is detected. From a system point of view, AGC adaptation should be held fixed or designed to attenuate or cancel the background noise if the VAD determines that voice is inactive.
- control signal 186 may be used to activate and deactivate the transmission subsystem 191.
- the transmission subsystem 191 is a wireless radio
- the wireless radio need only be activated or fully powered when voice activity is detected. In this way, the transmission power may be reduced when no voice activity is detected. Since the local radio system is likely powered by battery, saving transmission power gives increased usability to the headset system.
- the signal transmitted from transmission system 191 is a Bluetooth signal 193 to be received by a corresponding Bluetooth receiver in a control module.
- VAD process 200 has two microphones, with a first one of the microphones positioned on the wireless headset so that it is closer to the speaker's mouth than the second microphone, as shown in block 206. Each respective microphone generates a respective microphone signal, as shown in block 207.
- the voice activity detector monitors the energy level in each of the microphone signals, and compares the measured energy level, as shown in block 208.
- the microphone signals are monitored for when the difference in energy levels between signals exceeds a predefined threshold. This threshold value may be static, or may adapt according to the acoustic environment. By comparing the magnitude of the energy levels, the voice activity detector may accurately determine if the energy spike was caused by the target user speaking. Typically, the comparison results in either:
- VAD process 250 has two microphones, with a first one of the microphones positioned on the wireless headset so that it is closer to the speaker's mouth than the second microphone, as shown in block 251. Each respective microphone generates a respective microphone signal, which is received into a signal separation process.
- the signal separation process generates a noise-dominant signal, as well as a signal having speech content, as shown in block 252.
- the voice activity detector monitors the energy level in each of the signals, and compares the measured energy level, as shown in block 253.
- the signals are monitored for when the difference in energy levels between the signals exceeds a predefined threshold. This threshold value may be static, or may adapt according to the acoustic environment. By comparing the magnitude of the energy levels, the voice activity detector may accurately determine if the energy spike was caused by the target user speaking. Typically, the comparison results in either:
- the noise-dominant signal having a higher energy level then the speech-content signal as shown in block 255.
- the difference between the energy levels of the signals exceeds the predefined threshold value. Since it is predetermined that the speech-content signal has the speech content, this relationship of energy levels indicates that the target user is not speaking, as shown in block 258; a control signal may be used to indicate that the signal is noise only.
- the processes described with reference to figure 3 and figure 4 are both used.
- the VAD makes one comparison using the microphone signals (figure 3) and another comparison using the outputs from the signal separation process (figure 4).
- a combination of energy differences between channels at the microphone recording level and the output of the ICA stage may be used to provide a robust assessment if the current processed frame contains desired speech or not.
- the two channel voice detection process has significant advantages over known single channel detectors. For example, a voice over a loudspeaker may cause the single channel detector to indicate that speech is present, while the two channel process will understand that the loudspeaker is farther away than the target speaker hence not giving rise to a large energy difference among channels, so will indicate that it is noise. Since the signal channel VAD based on energy measures alone is so unreliable, its utility was greatly limited and needed to be complemented by additional criteria like zero crossing rates or a priori desired speaker speech time and frequency models. However, the robustness and accuracy of the two channel process enables the VAD to take a central role in supervising, controlling, and adjusting the operation of the wireless headset.
- the mechanism in which the VAD detects digital voice samples that do not contain active speech can be implemented in a variety of ways.
- One such mechanism entails monitoring the energy level of the digital voice samples over short periods (where a period length is typically in the range of about 10 to 30 msec). If the energy level difference between channels exceeds a fixed threshold, the digital voice samples are declared active, otherwise they are declared inactive.
- the threshold level of the VAD can be adaptive and the background noise energy can be tracked. This too can be implemented in a variety of ways. In one embodiment, if the energy in the current period is sufficiently larger than a particular threshold, such as the background noise estimate by a comfort noise estimator, the digital voice samples are declared active, otherwise they are declared inactive.
- a single channel VAD utilizing an adaptive threshold level
- speech parameters such as the zero crossing rate, spectral tilt, energy and spectral dynamics are measured and compared to values for noise. If the parameters for the voice differ significantly from the parameters for noise, it is an indication that active speech is present even if the energy level of the digital voice samples is low.
- comparison can be made between the differing channels, particularly the voice-centric channel (e.g., voice + noise or otherwise) in comparison to an other channel, whether this other channel is the separated noise channel, the noise centric channel which may or may not have been enhanced or separated (e.g., noise + voice), or a stored or estimated value for the noise.
- voice-centric channel e.g., voice + noise or otherwise
- the spectral dynamics of the digital voice samples against a fixed threshold may be useful in discriminating between long voice segments with audio spectra and long term background noise.
- the VAD performs auto-correlations using Itakura or Itakura-Saito distortion to compare long term estimates based on background noise to short term estimates based on a period of digital voice samples.
- line spectrum pairs LSPs
- FFT methods can be used when the spectrum is available from another software module.
- hangover should be applied to the end of active periods of the digital voice samples with active speech.
- Hangover bridges short inactive segments to ensure that quiet trailing, unvoiced sounds (such as /s/) or low SNR transition content are classified as active.
- the amount of hangover can be adjusted according to the mode of operation of the VAD. If a period following a long active period is clearly inactive (i.e., very low energy with a spectrum similar to the measured background noise) the length of the hangover period can be reduced. Generally, a range of about 20 to 500 msec of inactive speech following an active speech burst will be declared active speech due to hangover.
- the threshold may be adjustable between approximately -100 and approximately -30 dBm with a default value of between approximately -60 dBm to about -50 dBm, the threshold depending on voice quality, system efficiency and bandwidth requirements, or the threshold level of hearing.
- the threshold may be adaptive to be a certain fixed or varying value above or equal to the value of the noise (e.g., from the other channel(s)).
- the VAD can be configured to operate in multiple modes so as to provide system tradeoffs between voice quality, system efficiency and bandwidth requirements.
- the VAD is always disabled and declares all digital voice samples as active speech.
- typical telephone conversations have as much as sixty percent silence or inactive content. Therefore, high bandwidth gains can be realized if digital voice samples are suppressed during these periods by an active VAD.
- a number of system efficiencies can be realized by the VAD, particularly an adaptive VAD, such as energy savings, decreased processing requirements, enhanced voice quality or improved user interface.
- an active VAD not only attempts to detect digital voice samples containing active speech, a high quality VAD can also detect and utilize the parameters of the digital voice (noise) samples (separated or unseparated), including the value range between the noise and the speech samples or the energy of the noise or voice.
- an active VAD particularly an adaptive VAD, enables a number of additional features which increase system efficiency, including modulating the separation and/ or post-(pre-)processing steps.
- a VAD which identifies digital voice samples as active speech can switch on or off the separation process or any pre-/ post-processing step, or alternatively, applying different or combinations of separation and/ or processing techniques. If the VAD does not identify active speech, the VAD can also modulate different processes including attenuating or canceling background noise, estimating the noise parameters or normalizing or modulating the signals and/ or hardware parameters.
- Process 325 has a first microphone 327 generating a first microphone signal and a second microphone 329 generating a second microphone signal.
- method 325 is illustrated with two microphones, it will be appreciated that more than two microphones and microphone signals may be used.
- the microphone signals are received into speech separation process 330.
- Speech separation process 330 may be, for example, a blind signal separation process. In a more specific example, speech separation process 330 may be an independent component analysis process.
- Speech separation process 330 generates a clean speech signal 331.
- Clean speech signal 331 is received into transmission subsystem 332.
- Transmission subsystem 332 may be for example, a Bluetooth radio, an IEEE 802.11 radio, or a wired connection. Further, it will be appreciated that the transmission may be to a local area radio module, or may be to a radio for a wide area infrastructure. In this way, transmitted signal 335 has information indicative of a clean speech signal.
- Communication process 350 has a first microphone 351 providing a first microphone signal to the speech separation process 354.
- a second microphone 352 provides a second microphone signal into speech separation process 354.
- Speech separation process 354 generates a clean speech signal 355, which is received into transmission subsystem 358.
- the transmission subsystem 358 may be for example a Bluetooth radio, an IEEE 802.11 radio, other such wireless standards, or a wired connection.
- the transmission subsystem transmits the transmission signal 362 to a control module or other remote radio.
- the clean speech signal 355 is also received by a side tone processing module 356.
- Side tone processing module 356 feeds an attenuated clean speech signal back to local speaker 360.
- side tone processing module 356 may adjust the volume of the side tone signal sent to speaker 360 responsive to local acoustic conditions.
- the speech separation process 354 may also output a signal indicative of noise volume.
- the side tone processing module 356 may be adjusted to output a higher level of clean speech signal as feedback to the user. It will be appreciated that other factors may be used in setting the attenuation level for the side tone processing signal.
- Communication process 400 has a first microphone 401 providing the first microphone signal to a speech separation process 405.
- a second microphone 402 provides a second microphone signal to speech separation process 405.
- the speech separation process 405 generates a relatively clean speech signal 406 as well as a signal indicative of the acoustic noise 407.
- a two channel voice activity detector 410 receives a pair of signals from the speech separation process for determining when speech is likely occurring, and generates a control signal 411 when speech is likely occurring.
- the voice activity detector 410 operates a VAD process as described with reference to figure 3 or figure 4.
- the control signal 411 may be used to activate or adjust a noise estimation process 413.
- the noise estimation process 413 may more accurately characterize the noise. This knowledge of the characteristics of the acoustic noise may then be used by noise reduction process 415 to more fully and accurately reduce noise. Since the speech signal 406 coming from speech separation process may have some noise component, the additional noise reduction process 415 may further improve the quality of the speech signal. In this way the signal received by transmission process 418 is of a better quality with a lower noise component. It will also be appreciated that the control signal 411 may be used to control other aspects of the communication process 400, such as the activation of the noise reduction process or the transmission process, or activation of the speech separation process.
- the energy of the noise sample can be utilized to modulate the energy of the output enhanced voice or the energy of speech of the far end user.
- the VAD can modulate the parameters of the signals before, during and after the invention process.
- the described separation process uses a set of at least two spaced-apart microphones.
- the microphones may have a relatively direct path to the speaker's voice. In such a path, the speaker's voice travels directly to each microphone, without any intervening physical obstruction.
- the microphones may be placed so that one has a relatively direct path, and the other is faced away from the speaker. It will be appreciated that specific microphone placement may be done according to intended acoustic environment, physical limitations, and available processing power, for example.
- the separation process may have more than two microphones for applications requiring more robust separation, or where placement constraints cause more microphones to be useful.
- a speaker may be placed in a position where the speaker is shielded from one or more microphones.
- additional microphones would be used to increase the likelihood that at least two microphones would have a direct path to the speaker's voice.
- Each of the microphones receives acoustic energy from the speech source as well as from the noise sources, and generates a composite microphone signal having both speech components and noise components. Since each of the microphones is separated from every other microphone, each microphone will generate a somewhat different composite signal. For example, the relative content of noise and speech may vary, as well as the timing and delay for each sound source.
- the composite signal generated at each microphone is received by a separation process.
- the separation process processes the received composite signals and generates a speech signal and a signal indicative of the noise.
- the separation process uses an independent component analysis (ICA) process for generating the two signals.
- ICA independent component analysis
- the ICA process filters the received composite signals using cross filters, which are preferably infinitive impulse response filters with nonlinear bounded functions.
- the nonlinear bounded functions are nonlinear functions with pre-determined maximum and minimum values that can be computed quickly, for example a sign function that returns as output either a positive or a negative value based on the input value.
- the separation process could use a blind signal source (BSS) process, or an application specific adaptive filter process using some degree of a priori knowledge about the acoustic environment to accomplish substantially similar signal separation.
- BSS blind signal source
- application specific adaptive filter process using some degree of a priori knowledge about the acoustic environment to accomplish substantially similar signal separation.
- Wireless headset system 450 is constructed as an earpiece with an integrated boom microphone.
- Wireless headset system 450 is illustrated in figure 8 from a left-hand side 451 and from a right hand side 452. It will be appreciated that a wireless headset or earpiece is just one of many physical arrangements that benefit from the communication processes discussed herein. For example, portable communication devices, mobile handsets, headsets, hands-free car kits, helmets, and other diverse devices may benefit from a more robust process for separating speech from a noisy environment.
- the microphones are preferred to be arranged on the divide line of a mobile device, not symmetrically on each side of the hardware. In this way, when the mobile device is being used, the same microphone is always positioned to most effectively receive the most speech, regardless of the position of communication device, e.g., the primary microphoine is positioned in such a way as to be closest to the speaker's mouth regardless of user positioning of the device. This consistent and predefined positioning enables the ICA process to have better default values, and to more easily identify the speech signal.
- Process 500 positions transducers to receive acoustic information and noise, and generate composite signals for further processing as shown in blocks 502 and 504.
- the composite signals are processed into channels as shown in block 506.
- process 506 includes a set of filters with adaptive filter coefficients. For example, if process 506 uses an ICA process, then process 506 has several filters, each having an adaptable and adjustable filter coefficient. As the process 506 operates, the coefficients are adjusted to improve separation performance, as shown in block 521, and the new coefficients are applied and used in the filter as shown in block 523.
- the process 506 typically generates two channels, which are identified in block 508. Specifically, one channel is identified as a noise- dominant signal, while the other channel is identified as a speech signal, which may be a combination of noise and information. As shown in block 515, the noise-dominant signal or the combination signal can be measured to detect a level of signal separation. For example, the noise-dominant signal can be measured to detect a level of speech component, and responsive to the measurement, the gain of microphone may be adjusted. This measurement and adjustment may be performed during operation of the process 500, or may be performed during set-up for the process.
- FIG 10 illustrates one embodiment 600 of an ICA or BSS processing function.
- the ICA processes described with reference to figures 10 and 11 are particularly well suited to headset designs as illustrated in figure 8.
- This construction has a well defined and predefined positioning of the microphones, and allow the two speech signals to be extracted from a relatively small "bubble" in front of the speaker's mouth.
- Input signals X 1 and X 2 are received from channels 610 and 620, respectively. Typically, each of these signals would come from at least one microphone, but it will be appreciated other sources may be used.
- Cross filters Wi and W 2 are applied to each of the input signals to produce a channel 630 of separated signals Ui and a channel 540 of separated signals U 2 .
- Channel 630 (speech channel) contains predominantly desired signals and channel 640 (noise channel) contains predominantly noise signals.
- speech channel and “noise channel” are used, the terms “speech” and “noise” are interchangeable based on desirability, e.g., it may be that one speech and/ or noise is desirable over other speeches and/ or noises.
- the method can also be used to separate the mixed noise signals from more than two sources.
- Infinitive impulse response filters are preferably used in the present processing process.
- An infinitive impulse response filter is a filter whose output signal is fed back into the filter as at least a part of an input signal.
- a finite impulse response filter is a filter whose output signal is not feedback as input.
- the cross filters W21 and W12 can have sparsely distributed coefficients over time to capture a long period of time delays.
- the cross filters W2iand Wi2 are gain factors with only one filter coefficient per filter, for example a delay gain factor for the time delay between the output signal and the feedback input signal and an amplitude gain factor for amplifying the input signal.
- the cross filters can each have dozens, hundreds or thousands of filter coefficients.
- the output signals Ui and U 2 can be further processed by a post processing sub-module, a de-noising module or a speech feature extraction module.
- the ICA learning rule has been explicitly derived to achieve blind source separation, its practical implementation to speech processing in an acoustic environment may lead to unstable behavior of the filtering scheme.
- the adaptation dynamics of W12 and similarly W21 have to be stable in the first place.
- the gain margin for such a system is low in general meaning that an increase in input gain, such as encountered with non stationary speech signals, can lead to instability and therefore exponential increase of weight coefficients.
- speech signals generally exhibit a sparse distribution with zero mean, the sign function will oscillate frequently in time and contribute to the unstable behavior.
- a large learning parameter is desired for fast convergence, there is an inherent trade-off between stability and performance since a large input gain will make the system more unstable.
- the known learning rule not only lead to instability, but also tend to oscillate due to the nonlinear sign function, especially when approaching the stability limit, leading to reverberation of the filtered output signals Ui (t) and U2(t).
- the adaptation rules for W12 and W21 need to be stabilized. If the learning rules for the filter coefficients are stable and the closed loop poles of the system transfer function from X to U are located within the unit circle, extensive analytical and empirical studies have shown that systems are stable in the BIBO (bounded input bounded output). The final corresponding objective of the overall processing scheme will thus be blind source separation of noisy speech signals under stability constraints.
- the scaling factor sc_fact is adapted based on the incoming input signal characteristics. For example, if the input is too high, this will lead to an increase in sc_fact, thus reducing the input amplitude. There is a compromise between performance and stability. Scaling the input down by sc_fact reduces the SNR which leads to diminished separation performance. The input should thus only be scaled to a degree necessary to ensure stability. Additional stabilizing can be achieved for the cross filters by running a filter architecture that accounts for short term fluctuation in weight coefficients at every sample, thereby avoiding associated reverberation. This adaptation rule filter can be viewed as time domain smoothing.
- Further filter smoothing can be performed in the frequency domain to enforce coherence of the converged separating filter over neighboring frequency bins. This can be conveniently done by zero tapping the K-tap filter to length L, then Fourier transforming this filter with increased time support followed by Inverse Transforming. Since the filter has effectively been windowed with a rectangular time domain window, it is correspondingly smoothed by a sine function in the frequency domain. This frequency domain smoothing can be accomplished at regular time intervals to periodically reinitialize the adapted filter coefficients to a coherent solution.
- the function f(x) is a nonlinear bounded function, namely a nonlinear function with a predetermined maximum value and a predetermined minimum value.
- f(x) is a nonlinear bounded function which quickly approaches the maximum value or the minimum value depending on the sign of the variable x.
- a sign function can be used as a simple bounded function.
- a sign function f(x) is a function with binary values of 1 or -1 depending on whether x is positive or negative.
- Example nonlinear bounded functions include, but are not limited to:
- filter coefficient quantization error effect Another factor which may affect separation performance is the filter coefficient quantization error effect. Because of the limited filter coefficient resolution, adaptation of filter coefficients will yield gradual additional separation improvements at a certain point and thus a consideration in determining convergence properties.
- the quantization error effect depends on a number of factors but is mainly a function of the filter length and the bit resolution used.
- the input scaling issues listed previously are also necessary in finite precision computations where they prevent numerical overflow. Because the convolutions involved in the filtering process could potentially add up to numbers larger than the available resolution range, the scaling factor has to ensure the filter input is sufficiently small to prevent this from happening.
- the present processing function receives input signals from at least two audio input channels, such as microphones.
- the number of audio input channels can be increased beyond the minimum of two channels.
- speech separation quality may improve, generally to the point where the number of input channels equals the number of audio signal sources.
- the sources of the input audio signals include a speaker, a background speaker, a background music source, and a general background noise produced by distant road noise and wind noise, then a four-channel speech separation system will normally outperform a two-channel system.
- more input channels are used, more filters and more computing power are required.
- less than the total number of sources can be implemented, so long as there is a channel for the desired separated signal(s) and the noise generally.
- the present processing sub-module and process can be used to separate more than two channels of input signals.
- one channel may contain substantially desired speech signal
- another channel may contain substantially noise signals from one noise source
- another channel may contain substantially audio signals from another noise source.
- one channel may include speech predominantly from one target user, while another channel may include speech predominantly from a different target user.
- a third channel may include noise, and be useful for further process the two speech channels. It will be appreciated that additional speech or target channels may be useful. .
- Some applications involve only one source of desired speech signals, in other applications there may be multiple sources of desired speech signals.
- teleconference applications or audio surveillance applications may require separating the speech signals of multiple speakers from background noise and from each other.
- the present process can be used to not only separate one source of speech signals from background noise, but also to separate one speaker's speech signals from another speaker's speech signals.
- the present invention will accommodate multiple sources so long as at least one microphone has a relatively direct path with the speaker.
- the present process separates sound signals into at least two channels, for example one channel dominated with noise signals (noise- dominant channel) and one channel for speech and noise signals (combination channel).
- channel 730 is the combination channel
- channel 740 is the noise-dominant channel. It is quite possible that the noise- dominant channel still contains some low level of speech signals. For example, if there are more than two significant sound sources and only two microphones, or if the two microphones are located close together but the sound sources are located far apart, then processing alone might not always fully separate the noise. The processed signals therefore may need additional speech processing to remove remaining levels of background noise and/ or to further improve the quality of the speech signals.
- a Wiener filter with the noise spectrum estimated using the noise-dominant output channel (a VAD is not typically needed as the second channel is noise-dominant only).
- the Wiener filter may also use non-speech time intervals detected with a voice activity detector to achieve better SNR for signals degraded by background noise with long time support.
- the bounded functions are only simplified approximations to the joint entropy calculations, and might not always reduce the signals' information redundancy completely. Therefore, after signals are separated using the present separation process, post processing may be performed to further improve the quality of the speech signals.
- noise signals in the noise-dominant channel have similar signal signatures as the noise signals in the combination channel
- those noise signals in the combination channel whose signatures are similar to the signatures of the noise-dominant channel signals should be filtered out in the speech processing functions. For example, spectral subtraction techniques can be used to perform such processing.
- the signatures of the signals in the noise channel are identified.
- the speech processing is more flexible because it analyzes the noise signature of the particular environment and removes noise signals that represent the particular environment. It is therefore less likely to be over-inclusive or under-inclusive in noise removal.
- the general ICA process is adjusted to provide an adaptive reset mechanism.
- a signal separation process 750 is illustrated in figure 12.
- Signal separation process 750 receives a first input signal 760 from a first microphone, and a second input signal 762 from a second microphone.
- the ICA process has filters which adapt during operation. As these filters adapt, the overall process may eventually become unstable, and the resulting signal becomes distorted or saturated. Upon the output signal becoming saturated, the filters need to be reset, which may result in an annoying "pop" in the generated speech signal 770.
- the ICA process 750 has a learning stage 752 and an output stage 756.
- the learning stage 752 employs a relatively aggressive ICA filter arrangement, but its output is used only to "teach" the output stage 756.
- the output stage 756 provides a smoothing function, and more slowly adapts to changing conditions.
- the output stage generates a signal having speech content 770, as well as a noise-dominant signal 773. In this way, the learning stage quickly adapts and directs the changes made to the output stage, while the output stage exhibits an inertia or resistance to change.
- the ICA reset process 765 monitors values in each stage, as well as the final output signal. Since the learning stage 752 is operating aggressively, it is likely that the learning stage 752 will saturate more often then the output stage 756.
- the learning stage filter coefficients 754 are reset to a default condition, and the learning ICA 752 has its filter history replaced with current sample values.
- the resulting "glitch” does not cause any perceptible or audible distortion. Instead, the change merely results in a different set of filter coefficients being sent to the output stage 756. But, since the output stage 756 changes relatively slowly, it too, does not generate any perceptible or audible distortion.
- the ICA process 750 is made to operate without substantial distortion due to resets. Of course, the output stage 756 may still occasionally need to be reset, which may result in the usual "pop". However, the occurrence is now relatively rare.
- a reset mechanism is desired that will create a stable separating ICA filtered output with minimal distortion and discontinuity perception in the resulting audio by the user. Since the saturation checks are evaluated on a batch of stereo buffer samples and after ICA filtering, the buffers should be chosen as small as practical since reset buffers from the ICA stage will be discarded and there is not enough time to redo the ICA filtering in the current sample period. The past filter history is reinitialized for both ICA filter stages with the current recorded input buffer values. The post processing stage will receive the current recorded speech+noise signal and the current recorded noise channel signal as reference. Since the ICA buffer sizes can be reduced to 4 ms, this results in an imperceptible discontinuity in the desired speaker voice output.
- the filter values 754 or 758 or taps are reset to predefined values. Since the headset or earpiece often has only a limited range of operating conditions, the default values for the taps may be selected to account for the expected operating arrangement. For example, the distance from each microphone to the speaker's mouth is usually held in a small range, and the expected frequency of the speaker's voice is likely to be in a relatively small range. Using these constraints, as well as actual operation values, a set of reasonably accurate tap values may be determined. By carefully selecting default values, the time for the ICA to perform expectable separation is reduced. Explicit constraints on the range of filter taps to constrain the possible solution space should be included. These constraints may be derived from directivity considerations or experimental values obtained through convergence to optimal solutions in previous experiments. It will also be appreciated that the default values may adapt over time and according to environmental conditions.
- a communication system may have more than one set 777 of default values.
- one set of default values e.g. "Set 1”
- another set of default values e.g., "Set 2”
- different sets of default values may be stored for different users. If more than one set of default values is provided, than a supervisory module 767 will be included that determines the current operating environment, and determines which of the available default value sets will be used. Then, when the reset command is received from the reset monitor 765, the supervisory process 767 will direct the selected default values to the ICA process filter coefficients, for example, by storing new default values in Flash memory on a chipset.
- the acoustic echo can be considered as interfering noise and removed by the same processing algorithm.
- the filter constraints on one cross filter reflect the need for removing the desired speaker from one channel and limit its solution range.
- the other crossfilter removes any possible outside interferences and the acoustic echo from a loudspeaker.
- the constraints on the second crossfilter taps are therefore determined by giving enough adaptation flexibility to remove the echo.
- the learning rate for this crossfilter may need to be changed too and may be different from the one needed for noise suppression.
- the relative position of the ear speaker to the microphones may be fixed.
- the necessary second crossfilter to remove the ear speaker speech can be learned in advanced and fixed.
- the transfer characteristics of the microphone may drift over time or as the environment such as temperature changes.
- the position of the microphones may be adjustable to some degree by the user. AU these require an adjustment of the crossfilter coefficients to better eliminate the echo. These coefficients may be constrained during adaptation to be around the fixed learned set of coefficients.
- the acoustics echo is removed from the microphone signal using the adaptive normalized least mean square (NLMS) algorithm and the far end signal as reference. Silence of the near end user needs to be detected and the signal picked up by the microphone is then assumed to contain only echo.
- the NLMS algorithm builds a linear filter model of the acoustic echo using the far end signal as the filter input, and the microphone signal as filter output. When it is detected that the both the far are near end users are talking, the learned filter is frozen and applied to the incoming far end signal to generate an estimate of the echo. This estimated echo is then subtracted from the microphone signal and the resulted signal is sent as echo cleaned.
- NLMS adaptive normalized least mean square
- the drawbacks of the above scheme are that it requires good detection of silence of near end user. This could be difficult to achieve if the user is in a noisy environment.
- the above scheme also assumes a linear process in the incoming far end electrical signal to the ear speaker to microphone pick-up path.
- the ear speaker is seldom a linear device when converting the electric signal to sound.
- the non-linear effect is pronounced when the speaker is driven at high volume. It may be saturated, produce harmonics or distortion.
- the distorted acoustic signal from the ear speaker will be picked up by both microphones.
- the echo will be estimated by the second cross- filter as U 2 and removed from the primary microphone by the first cross-filter. This results in an echo free signal Ui.
- This scheme eliminates the need to model the non-linearity of the far end signal to microphone path.
- the learning rules (3- 4) operate regardless if the near end user is silent. This gets rid of a double talk detector and the cross-filters can be updated throughout the conversation.
- the near end microphone signal and the incoming far end signal can be used as the input Xi and X2.
- the algorithm described in this patent can still be applied to remove the echo.
- the only modification is the weights be all set zero as the far end signal X2 would not contain any near end speech.
- Learning rule (4) will be removed as a result.
- the cross-filter can still be updated throughout the conversation and there is no need for a double talk detector.
- conventional echo suppression methods can still be applied to remove any residual echo. These methods include acoustic echo suppression and complementary comb filtering.
- signal to the ear speaker is first passed through the bands of comb filter.
- the microphone is coupled to a complementary comb filter whose stop bands are the pass band of the first filter.
- the microphone signal is attenuated by 6dB or more when the near end user is detected to be silence.
- Speech separation process 808 has a microphone 801 that is positioned closer to a target speaker then microphone 802. In this way, microphone 801 will generate a stronger speech signal, while microphone 802 will have a more dominant noise signal.
- the communication process 800 has a signal separation process 808, for example, a BSS or ICA process.
- the signal separation process generates a signal having speech content 812, as well as a noise-dominant signal 814.
- the communication process 800 has post-processing steps 810 where additional noise is removed from the speech-content signal 812. In one example, a noise signature is used to spectrally subtract noise from the speech signal 812.
- the communication process 800 may apply scaling 805 or 806 to the input to the ICA/BSS process.
- scaling 805 or 806 may be applied to match the noise signature and amplitude in each frequency bin between voice+noise and noise-only channels.
- the left and right input channels may be scaled with respect to each other so a close as possible model of the noise in the voice+noise channel is obtained from the noise channel.
- the Over-Subtraction Factor (OSF) factor instead of tuning the Over-Subtraction Factor (OSF) factor in the processing stage, this scaling generally yields better voice quality since the ICA stage is forced to remove as much directional components of the isotropic noise as possible.
- the noise-dominant signal from microphone 802 may be more aggressively amplified 805 when additional noise reduction is needed. In this way, the ICA/ BSS process 808 provides additional separation, and less post processing is needed.
- Real microphones may have frequency and sensitivity mismatch while the ICA stage may yield incomplete separation of high/ low frequencies in each channel. Individual scaling of the OSF in each frequency bin or range of bins may therefore be necessary to achieve the best voice quality possible. Also, selected frequency bins may be emphasized or de-emphasized to improve perception.
- the input levels from the microphones 801 and 802 may also be independently adjusted according to a desired ICA/ BSS learning rate or to allow more effective application of post processing methods.
- the ICA/BSS and post processing sample buffers evolve through a diverse range of amplitudes. Downscaling of the ICA learning rate is desirable at high input levels. For example, at high input levels, the ICA filter values may rapidly change, and more quickly saturate or become unstable. By scaling or attenuating the input signals, the learning rate may be appropriately reduced. Downscaling of the post processing input is also desirable to avoid computing rough estimates of speech and noise power resulting in distortion.
- adaptive scaling of input data to ICA/BSS 808 and post processing 810 stages may be applied.
- sound quality may be enhanced overall by suitably choosing high intermediate stage output buffer resolution compared to the DSP input/ output resolution.
- Independent input scaling may also be used to assist in amplitude calibration between the two microphones 801 and 802. As described earlier, it is desirable that the two microphones 801 and 802 be properly matched. Although some calibration may be done dynamically, other calibrations and selections may be done in the manufacturing process. Calibration of both microphones to match frequency and overall sensitivities should be performed to minimize tuning in ICA and post processing stage. This may require inversion of the frequency response of one microphone to achieve the response of another. All techniques known in the literature to achieve channel inversion, including blind channel inversion, can be used to this end. Hardware calibration can be performed by suitably matching microphones from a pool of production microphones. Offline or online tuning can be considered. Online tuning will require the help of the VAD to adjust calibration settings in noise-only time intervals i.e. the microphone frequency range needs to be excited preferentially by white noise to be able to correct all frequencies.
- Wind noise is typically caused by a extended force of air being applied directly to a microphone's transducer membrane.
- the highly sensitive membrane generates a large, and sometimes saturated, electronic signal.
- the signal overwhelms and often decimates any useful information in the microphone signal, including any speech content.
- the wind noise since the wind noise is so strong, it may cause saturation and stability problems in the signal separation process, as well as in post processing steps. Also, any wind noise that is transmitted causes an unpleasant and uncomfortable listening experience to the listener. Unfortunately, wind noise has been a particularly difficult problem with headset and earpiece devices.
- the two-microphone arrangement of the wireless headset enables a more robust way to detect wind, and a microphone arrangement or design that minimizes the disturbing effects of wind noise.
- a two channel wind noise reduction process 900 is illustrated in figure 14. Since the wireless headset has two microphones, the headset may operate a process 900 that more accurately identifies the presence of wind noise. As described above, the two microphones may be arranged so that their input ports face different directions as shown in block 902, or are shielded to each receive wind from a different direction. In such an arrangement, a burst of wind will cause a dramatic energy level increase in the microphone facing the wind, while the other microphone will only be minimally affected.
- the headset may determine that that microphone is being subjected to wind. Further, other processes may be applied to the microphone signal to further confirm that the spike is due to wind noise. For example, wind noise typically has a low-frequency pattern, and when such a pattern is found on one or both channels, the presence of wind noise may be indicated as shown in block 904. Alternatively, specific mechanical or engineering designs can be considered for wind noise.
- the headset may operate a process to minimize the wind's effect. For example, the process may block the signal from the microphone that is subjected to wind, and process only the other microphone's signal as shown in block 906. In this case, the separation process is also deactivated, and the noise reduction processes operated as a more traditional single microphone system as shown in block 908.
- the headset may return to normal two channel operation as shown in block 913.
- the microphone that is farther from the speaker receives such a limited level of speech signal that it is not able to operate as a sole microphone input. In such a case, the microphone closest to the speaker can not be deactivated or de-emphasized, even when it is being subjected to wind.
- the wireless headset may advantageous be used in windy environments.
- the headset has a mechanical knob on the outside of the headset so the user can switch from a dual channel mode to a single channel mode. If the individual microphones are directional, then even single microphone operation may still be too sensitive to wind noise. However when the individual microphones are omnidirectional, the wind noise artifacts should be somewhat alleviated, although the acoustical noise suppression will deteriorate.
- aspects of the invention 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 invention are embodied as software at least one stage during manufacturing (e.g. before being embedded in firmware or in a PLD), the software may be carried by any computer readable medium, such as magnetically- or optically-readable disks (fixed or floppy), modulated on a carrier signal or otherwise transmitted, etc.
- aspects of the invention 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) 7 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
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