CA2354755A1 - Sound intelligibilty enhancement using a psychoacoustic model and an oversampled filterbank - Google Patents
Sound intelligibilty enhancement using a psychoacoustic model and an oversampled filterbank Download PDFInfo
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- CA2354755A1 CA2354755A1 CA002354755A CA2354755A CA2354755A1 CA 2354755 A1 CA2354755 A1 CA 2354755A1 CA 002354755 A CA002354755 A CA 002354755A CA 2354755 A CA2354755 A CA 2354755A CA 2354755 A1 CA2354755 A1 CA 2354755A1
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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
-
- 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
- G10L21/0316—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude
- G10L21/0364—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude for improving intelligibility
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
- G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
- G10L19/0204—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders using subband decomposition
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
- G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
- G10L19/0212—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders using orthogonal transformation
-
- 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
- G10L2021/02168—Noise filtering characterised by the method used for estimating noise the estimation exclusively taking place during speech pauses
-
- 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
- G10L21/0264—Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R2499/00—Aspects covered by H04R or H04S not otherwise provided for in their subgroups
- H04R2499/10—General applications
- H04R2499/13—Acoustic transducers and sound field adaptation in vehicles
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R25/00—Deaf-aid sets, i.e. electro-acoustic or electro-mechanical hearing aids; Electric tinnitus maskers providing an auditory perception
- H04R25/35—Deaf-aid sets, i.e. electro-acoustic or electro-mechanical hearing aids; Electric tinnitus maskers providing an auditory perception using translation techniques
- H04R25/356—Amplitude, e.g. amplitude shift or compression
Abstract
A sound intelligibility enhancement system is disclosed. The system uses a psychoacoustic model and an oversampled filterbank where the level of a signal-of-interest that falls below the environmental noise is selectively amplified as a function of the input level so that it is audible above the noise.
Description
MODEL AND AN OVERSAMPLED FILTERBANK
Field of the Invention The present invention relates to audio reproduction applications where a desired audio signal is received in an uncontaminated form and interference (e.g., environmental noise) is present as an acoustic signal. This invention can be used in any application where it is necessary to improve the intelligibility of the received audio signal while maintaining high fidelity and good signal quality.
Specific applications of the invention include headsets used in call o centers, with mobile phones or with other miniaturelportable audio devices in noisy environments (e.g., aircraft, concerts, factories, etc.) and in any other applications where an uncontaminated representation of a desired signal is available and improved signal intelligibility and high signal quality are desired.
Background of the Invention In acoustically noisy environments, listeners often have difficulty hearing a desired audio signal (hereinafter, referred to as a "signal-of-interest").
For example, a cellular phone user in an automobile may have difficulty understanding the received speech signal through their headset because the noise of the automobile overpowers the signal-of-interest (i.e., the speech signal 2o received by the cell phone).
Many attempts have been made to solve this problem as follows:
(a) Passive noise attenuating headsets: For the specific application in headset applications, passive noise attenuation is provided by a large and bulky earcup that physically isolates the environmental (acoustic) noise from the users ear.
(b) Amplification: The incoming electrical signal is amplified to overcome the background noise level. If not properly controlled, this can result in dangerously loud output levels.
(c) Filtering: The signal is statically filtered to make it more intelligible (d) Simple Automatic Gain Control (AGC): The signal-of-interest is passed through an automatic gain control (AGC) system in which gain is adjusted based on a level measurement of the noise inside or outside the earcup. The gain of the AGC is typically controlled by a simple measurement of the overall noise level.
(e) Active noise cancellation (ANC): Anti-noise (generated using either an open- or closed-loop servo system) is generated and added acoustically to the noise signal. For headset applications, see [1, 2].
(f) Sometimes, these methods are combined: a common scheme for a headset application is to combine a passive noise-attenuating headset with an ~5 ANC system [1].
Although these methods are highly effective and reduce the noise for a wide range of applications, they are not always suitable. For example, ANC
requires an accurate noise reference, which may not be available and works only at lower frequencies. Passive noise reduction works well only if sufficient 20 room is available for the sound insulation. Filtering distorts the signal frequency content. AGC systems do not consider the human auditory system and yield sub-optimal results. Also, even when these solutions can be applied, applications exist where the power drain of these solutions is prohibitive and a miniature, low power technique is required.
25 Accordingly, there is a need to solve the problems noted above and also a need for an innovative approach to enhance andlor replace the current technologies.
Summar~and Advantages of the Invention Signal processing algorithms for audio listening applications are commonly called "receive algorithms" (RX) because the listener wants to hear the received audio signal. A typical application for the Signal Intelligibility Enhancement (hereinafter, referred to as the "SIE") processing is a headset being used in a noisy environment (see Fig. 1 ).
The listener hears a combination of the desired, electrical signal (e.g., a signal from a cellular phone) and the environmental noise (an undesired signal that may reduce the intelligibility of the signal-of-interest). In headset applications, the passive attenuation provided by the headset further improves the performance by reducing the audible level of the ambient noise.
According to the present invention, the SIE algorithm utilizes a measurement of either (1 ) the level of the outside interference (undesired signal, noise) or (2) the level of the interference (undesired signal, noise) in the ear ~5 canal to adaptively adjust the gain and equalization of the signal-of-interest (electrical) so that the intelligibility and audibility of the signal-of-interest is improved. These level measurements are made using frequency band levels alone on in combination using techniques that are well-known in the art [5,6,9].
If the level of signal-of-interest falls significantly below the level of the 2o noise signal in the ear canal, the signal-of-interest is masked and can be inaudible. The listener also has a maximum signal level that is considered comfortable (Loudness Discomfort Level - LDL). The difference in level between the level of the noise signal and the LDL which are both functions of frequency is the effective dynamic range also a function of frequency. Because of the level of 25 the undesired signal (i.e. noise), the listener experiences reduced dynamic range. Remapping the dynamic range of the signal-of-interest in a frequency dependent manner raises its level above the ambient noise making the signal-of-interest audible. However, the amplification must not allow the level of the signal to exceed the maximum signal level that is comfortable for the listener (LDL). The solution is to map the dynamic range of the signal-of-interest into the available dynamic range of the signal in noise (see Fig. 2).
Fig. 3 shows how this operation works as a function of frequency. In frequency regions where the level of the signal-of-interest falls significantly below the noise, the signal is selectively amplified as a function of the input level so that it is audible above the noise floor.
This type of signal processing is called dynamic range compression. For an application like this it is advantageously implemented in a plurality of overlapping or non-overlapping frequency bands where the bands can be ~o processed independently or grouped into channels and processed together.
To provide the best possible fidelity, ultra miniaturized size and the lowest possible power consumption, the SIE algorithm is implemented using an oversampled filterbank to separate the both the signal-of-interest and the undesired signal into a plurality of overlapping, abutting or non-overlapping 15 bands [3]. The design is advantageously implemented in an architecture that combines a weighted overlap add (WOLA) filterbank, a software programmable DSP core, an input-output processor and non-volatile memory [4].
The Signal Intelligibility Enhancement of the invention can be used in environments where there are very high levels of noise relative to the level of the 2o signal-of-interest. This can result in a very small available dynamic range. While it is possible to use dynamic range compression to map the signal-of-interest into this small dynamic range, the resulting signal fidelity and quality may suffer.
(Note that the goal of dynamic range compression is to purposely distort the dynamic range of the signal while minimizing the perceived distortion.) In this 2s situation, applying the minimum gain required to make the signal-of-interest level audible over the desired noise (and therefore more intelligible) results in improved signal quality.
According to the present invention, the SIE processing also incorporates a psychoacoustic model that calculates, on an on-going basis, the minimum amplification that must be applied to make the signal-of-interest audible over the undesired signal. This results in better fidelity and signal quality.
Other features and aspects of the present invention, and the advantages associated therewith will be described below:
1 ) Signal intelligibility is improved. At the same time, signal fidelity and quality are maintained, and perceived quality can improve in noisy environments.
The design can be implemented using ultra low-power, sub-miniature technology that is suitable for incorporation directly into a headset or other low-power, portable audio applications [4].
~0 2) The use of psychoacoustic models and high-fidelity, constrained dynamic range adaptation means that the utility of the dynamic range is maximized (where dynamic range is the level difference between the minimum signal level that is audible above the noise and the maximum allowable signal level). This results in excellent signal quality and fidelity.
~5 3) An implementation in an oversampled filterbank [3] provides a high-fidelity, ultra low-power solution that is ideal for portable, low-power audio applications.
4) When combined with a closed-loop, active noise cancellation (ANC) system, the same microphone (located near the output transducer) can be used 2o for both the measurement of the signal to generate the "anti-noise" and the residual level measurement needed to provide the input level estimate required for the signal intelligibility enhancement processing. This combined approach works better than either method alone because ANC is limited to providing benefit at low frequencies (because of design considerations) and the signal 2s intelligibility enhancement provides benefit at higher frequencies. Using the same microphone reduces costs and simplifies the system. In many listening situations, low-frequency noise dominates. Here, the use of ANC at low frequencies to reduce the noise increases the available dynamic range, which results in improved fidelity relative to either method (ANC or SIE) being used alone.
5) Sometimes the signal-of-interest will contain noise. Using a psychoacoustic model andlor low-level expansion, the signal-of-interest can be processed such that the noise is placed below the acoustic signal level (or the residual signal level if ANC is being applied). When this is properly implemented, the listener perceives less noise.
6) It is also possible to incorporate single-microphone noise reduction techniques into the signal-of-interest channel [8]. This provides a signal for the listener that is more audible (relative to the environmental noise) and less tiring to listen to for extended periods of time because the processed signal-of-interest contains less noise.
7) When used with a Signal Activity Detector (SAD), an implementation is able to differentiate between a signal-of-interest and the environmental noise (interference). This ensures that the estimate of the noise signal does not become contaminated with the signal-of-interest, allowing voice communications to be clearer with higher intelligibility.
8) In an alternative realization, an adaptive filter is used to correlate the contaminated signal (signal + noise) with the uncontaminated electrical signal so 2o that an estimate of the noise can be derived. This provides a more reliable estimate of the noise signal that is contaminating the signal-of-interest.
Employing this technique provides improved signal fidelity.
9) In an alternative realization, a spectral differencing technique is used to estimate the spectral content of the environmental noise. This provides a more 25 reliable estimate of the noise signal that is contaminating the signal-of-interesting. This processing also improves signal fidelity.
10) With a multiband implementation of the compressor component (ranges of frequency are treated independently as opposed to compressing the entire spectrum uniformly) the overall perceived audio quality is improved [6].
Treating frequency bands independently of one another allows for greater freedom to produce high-fidelity compression. Furthermore, constraining the relative compression levels of the frequency ranges so a pre-determined maximum amount of frequency shaping may occur, maintains the signal quality across a wide range of noise environments. This ensures that frequency localized noise sources are better handled.
11 ) Using a multiband and/or adaptive level measurement of the noise allows an implementation to smoothly handle any changes of noise environment. It also protects against undesirable distortion, which would otherwise be caused by drastic changes in the environmental noise [5, 6].
12) A safety system is implicitly incorporated into the invention. The signal processing does not amplify desired sounds above the user's Loudness Discomfort Level (LDL). This is a safety feature designed to help protect the user's hearing in very high noise environments. It, along with the other ~5 adjustments provided by the invention, provide the opportunity to personalize an implementation to a specific user.
13) In summary, by using the invention, the user can receive a signal with improved SNR (signal-to-noise ratio) that continuously adapts to the user's environment, rendering the signal-of-interest at a comfortable level. This results 2o in improved signal intelligibility, improved perceived signal quality and less user fatigue.
A further understanding of the other features, aspects, and advantages of the present invention will be realized by reference to the following description, appended claims, and accompanying drawings.
2s Brief Description of the Drawings Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
Figure 1 illustrates a typical situation for a receive algorithm;
Figure 2 a schematic representation of a dynamic range mapping of signal-of-interest into available dynamic range;
Figure 3 shows a basic operation ofithe signal intelligibility enhancement according to the present invention;
Figure 4 shows a high-level block diagram of SIE processing according to the invention, incorporating a SAD;
Figure 5 shows a block diagram of SIE using adaptive noise estimation;
Figure 6 shows a block diagram of SIE using spectral differencing noise estimation;
Figure 7 show the input/gain function for straight-line compression;
Figure 8 shows one embodiment of the invention with SIE and ANC
combined;
Figure 9 is a diagram illustrating combining left and right noise floors;
Figure 10 shows a binaural combination system with transmit algorithm ~5 capability;
Figure 11 is a block diagram showing an open-loop SIE with shared TX
microphone; and Figure 12 is a block diagram showing an open-loop SIE with shared TX
microphones and directional processing.
20 [Note: In all the figures, a block labeled 'A' represents an analysis by a filterbank - preferably this is an oversampled WOLA filterbank, but this is not always a requirement. A block labeled 'S' represents a filterbank synthesis.
Again, it is preferable in most cases that this is an oversampled WOLA
filterbank, but this is not always necessary.]
25 Detailed Description of the Preferred Embodirnent(s) The preferred embodiment will be described with particular reference to a headset, to which the present invention is principally applied, but not exclusively.
Fig. 4 shows a block diagram of the invention.
Referring to Fig. 4, an acoustic input receives the environmental noise, possibly contaminated with the signal-of-interest or other signals (e.g., speech to be transmitted) from a microphone that is located either inside the ear canal (closed-loop implementation) or outside the ear canal (open-loop implementation).
In a closed loop implementation, equalization is included to account for ~o the acoustics of the signal path (e.g., an acoustic tube that supplies audio to a microphone molded into the earcup).
In an open loop implementation, a model of the transfer function from the microphone to the inside of the ear canal is incorporated to account for the attenuation and frequency response of the headset earcup and acoustic signal 15 path. A model of the output stage can also be included so that the level of the signal-of-interest that may appear in the ear canal, prior to any adaptive equalization, can be approximated.
In an open-loop implementation, a separate or shared environmental noise microphone can be used. In the shared microphone case, the same 2o microphone can be used for transmitting a signal (e.g., transmitted speech in a headset application). This reduces costs and simplifies mechanical construction.
In operation, the psychoacoustic model receives the level of the signal of interest in frequency bands or combinations of frequency bands (the desired signal spectrum) and using the level of environmental noise in frequency bands 2s or combinations frequency hands (the noise spectrum), computes dynamic range parameters that are applied by the multi-band compressor. The multi-band compressor then uses the dynamic range parameters supplied by the psychoacoustic model to equalize the signal as a function of frequency to improve its audibility. The use of a psychoacoustic model, combined with well-known dynamic range compression techniques, ensures that the output audio is made audible and intelligible over the environmental noise while minimizing perceived distortion and maintaining the quality of the desired signal.
Noise Estimation s An important input to the SIE signal processing is the spectrum of the environmental noise.
This can be supplied via a secondary input that receives the environmental noise by some method, such as a microphone. It can also be obtained by using a shared-input microphone (see below).
The SIE processing of the invention employs either a desired signal activity detector (DSAD), an adaptive estimation technique or a spectral differencing technique to obtain an accurate, uncontaminated estimate of the environmental noise spectrum.
The DSAD employs techniques well-known in the art to sample the spectrum of the signal when the desired signal is not present (i.e., during pauses or breaks in the desired signal). This ensures that the algorithm does not consider the desired signal (or in the case of a headset application with a shared microphone, the transmitted speech) to be part of the environmental noise.
In closed-loop implementations, when the DSAD indicates that there is no 2o desired signal present, the noise spectral image is updated. In an open-loop implementation, the DSAD may optionally monitor the environmental noise signal to ensure that transmitted speech or other signals of interest do not contaminate the noise spectrum that is supplied as an input to the psychoacoustic model.
2s If, in a closed-loop implementation, the noise spectrum has not been updated for some predetermined time period, the output audio may optionally mute for a brief period of time so that the noise spectrum can be updated without the desired signal being present. Using the DSAD in combination with timed updates (when necessary) ensures that noise spectrum is always current and that it is never contaminated with the desired signal spectrum.
Adaptive noise estimation employs techniques that are well-known in the art [11] to estimate the environmental noise. Fig. 5 shows a time domain technique, but it is understood that frequency domain techniques are also possible and rnay be advantageous. Thus, it is also possible to employ an oversampled, sub-band approach [see the co-pending patent application, which is filed on the same day by the present applicant entitled "Subband Adaptive Processing in an Oversampled Filterbank," and the disclosure of which is 1o incorporated herein by reference thereto.] Fig. 5 shows a block diagram of SIE
with Adaptive Noise Estimation.
Adaptive noise estimation requires no breaks in the desired signal to estimate the noise. The noise is continuously estimated using the correlation between the contaminated signal and the electrical signal. The adaptive correlator output contains primarily the signal components that are uncorrelated between the desired signal and the desired signal plus noise.
Spectral differencing takes the difference between a filtered or unfiltered version of the frequency domain representation of the signal-of-interest and the frequency domain representation of the environmental noise signal. This 2o subtraction can be done in bands or groups of bands. This estimation method is especially advantageous in closed-loop implementations (see below) where the environmental noise signal also contains the signal-of-interest because of the acoustic summation of the environmental noise and SIE processed signal-of-interest.
Filtering the signal-of-interest can be employed to derive a more accurate estimate. If the filter has a frequency response equivalent or approximately equivalent to the frequency response of.the output stage (SIE equalization, amplifier, loudspeaker and acoustics) and microphone, then the subtraction in the frequency domain provides an excellent approximation to the 3o uncontaminated (with the signal-of-interest) environmental noise.
Like adaptive estimation, spectral differencing requires no breaks in the desired signal to estimate the noise - the noise is continuously estimated using the spectral difference between the two signals (Fig. 6).
Psychoacoustic Processing s Four different strategies for the psychoacoustic model, and combinations thereof, can be employed to calculate the gains that are supplied as input to the multiband compressor. The gains are computed to ensure that the processed version of the desired signal is always audible over the environmental noise and that it is always comfortable for the listener. In all cases the LDL gives the upper limit of the dynamic range.
1 ) The lower limit of the dynamic range is set by the energy of the environmental noise within a frequency band or combination of bands.
Field of the Invention The present invention relates to audio reproduction applications where a desired audio signal is received in an uncontaminated form and interference (e.g., environmental noise) is present as an acoustic signal. This invention can be used in any application where it is necessary to improve the intelligibility of the received audio signal while maintaining high fidelity and good signal quality.
Specific applications of the invention include headsets used in call o centers, with mobile phones or with other miniaturelportable audio devices in noisy environments (e.g., aircraft, concerts, factories, etc.) and in any other applications where an uncontaminated representation of a desired signal is available and improved signal intelligibility and high signal quality are desired.
Background of the Invention In acoustically noisy environments, listeners often have difficulty hearing a desired audio signal (hereinafter, referred to as a "signal-of-interest").
For example, a cellular phone user in an automobile may have difficulty understanding the received speech signal through their headset because the noise of the automobile overpowers the signal-of-interest (i.e., the speech signal 2o received by the cell phone).
Many attempts have been made to solve this problem as follows:
(a) Passive noise attenuating headsets: For the specific application in headset applications, passive noise attenuation is provided by a large and bulky earcup that physically isolates the environmental (acoustic) noise from the users ear.
(b) Amplification: The incoming electrical signal is amplified to overcome the background noise level. If not properly controlled, this can result in dangerously loud output levels.
(c) Filtering: The signal is statically filtered to make it more intelligible (d) Simple Automatic Gain Control (AGC): The signal-of-interest is passed through an automatic gain control (AGC) system in which gain is adjusted based on a level measurement of the noise inside or outside the earcup. The gain of the AGC is typically controlled by a simple measurement of the overall noise level.
(e) Active noise cancellation (ANC): Anti-noise (generated using either an open- or closed-loop servo system) is generated and added acoustically to the noise signal. For headset applications, see [1, 2].
(f) Sometimes, these methods are combined: a common scheme for a headset application is to combine a passive noise-attenuating headset with an ~5 ANC system [1].
Although these methods are highly effective and reduce the noise for a wide range of applications, they are not always suitable. For example, ANC
requires an accurate noise reference, which may not be available and works only at lower frequencies. Passive noise reduction works well only if sufficient 20 room is available for the sound insulation. Filtering distorts the signal frequency content. AGC systems do not consider the human auditory system and yield sub-optimal results. Also, even when these solutions can be applied, applications exist where the power drain of these solutions is prohibitive and a miniature, low power technique is required.
25 Accordingly, there is a need to solve the problems noted above and also a need for an innovative approach to enhance andlor replace the current technologies.
Summar~and Advantages of the Invention Signal processing algorithms for audio listening applications are commonly called "receive algorithms" (RX) because the listener wants to hear the received audio signal. A typical application for the Signal Intelligibility Enhancement (hereinafter, referred to as the "SIE") processing is a headset being used in a noisy environment (see Fig. 1 ).
The listener hears a combination of the desired, electrical signal (e.g., a signal from a cellular phone) and the environmental noise (an undesired signal that may reduce the intelligibility of the signal-of-interest). In headset applications, the passive attenuation provided by the headset further improves the performance by reducing the audible level of the ambient noise.
According to the present invention, the SIE algorithm utilizes a measurement of either (1 ) the level of the outside interference (undesired signal, noise) or (2) the level of the interference (undesired signal, noise) in the ear ~5 canal to adaptively adjust the gain and equalization of the signal-of-interest (electrical) so that the intelligibility and audibility of the signal-of-interest is improved. These level measurements are made using frequency band levels alone on in combination using techniques that are well-known in the art [5,6,9].
If the level of signal-of-interest falls significantly below the level of the 2o noise signal in the ear canal, the signal-of-interest is masked and can be inaudible. The listener also has a maximum signal level that is considered comfortable (Loudness Discomfort Level - LDL). The difference in level between the level of the noise signal and the LDL which are both functions of frequency is the effective dynamic range also a function of frequency. Because of the level of 25 the undesired signal (i.e. noise), the listener experiences reduced dynamic range. Remapping the dynamic range of the signal-of-interest in a frequency dependent manner raises its level above the ambient noise making the signal-of-interest audible. However, the amplification must not allow the level of the signal to exceed the maximum signal level that is comfortable for the listener (LDL). The solution is to map the dynamic range of the signal-of-interest into the available dynamic range of the signal in noise (see Fig. 2).
Fig. 3 shows how this operation works as a function of frequency. In frequency regions where the level of the signal-of-interest falls significantly below the noise, the signal is selectively amplified as a function of the input level so that it is audible above the noise floor.
This type of signal processing is called dynamic range compression. For an application like this it is advantageously implemented in a plurality of overlapping or non-overlapping frequency bands where the bands can be ~o processed independently or grouped into channels and processed together.
To provide the best possible fidelity, ultra miniaturized size and the lowest possible power consumption, the SIE algorithm is implemented using an oversampled filterbank to separate the both the signal-of-interest and the undesired signal into a plurality of overlapping, abutting or non-overlapping 15 bands [3]. The design is advantageously implemented in an architecture that combines a weighted overlap add (WOLA) filterbank, a software programmable DSP core, an input-output processor and non-volatile memory [4].
The Signal Intelligibility Enhancement of the invention can be used in environments where there are very high levels of noise relative to the level of the 2o signal-of-interest. This can result in a very small available dynamic range. While it is possible to use dynamic range compression to map the signal-of-interest into this small dynamic range, the resulting signal fidelity and quality may suffer.
(Note that the goal of dynamic range compression is to purposely distort the dynamic range of the signal while minimizing the perceived distortion.) In this 2s situation, applying the minimum gain required to make the signal-of-interest level audible over the desired noise (and therefore more intelligible) results in improved signal quality.
According to the present invention, the SIE processing also incorporates a psychoacoustic model that calculates, on an on-going basis, the minimum amplification that must be applied to make the signal-of-interest audible over the undesired signal. This results in better fidelity and signal quality.
Other features and aspects of the present invention, and the advantages associated therewith will be described below:
1 ) Signal intelligibility is improved. At the same time, signal fidelity and quality are maintained, and perceived quality can improve in noisy environments.
The design can be implemented using ultra low-power, sub-miniature technology that is suitable for incorporation directly into a headset or other low-power, portable audio applications [4].
~0 2) The use of psychoacoustic models and high-fidelity, constrained dynamic range adaptation means that the utility of the dynamic range is maximized (where dynamic range is the level difference between the minimum signal level that is audible above the noise and the maximum allowable signal level). This results in excellent signal quality and fidelity.
~5 3) An implementation in an oversampled filterbank [3] provides a high-fidelity, ultra low-power solution that is ideal for portable, low-power audio applications.
4) When combined with a closed-loop, active noise cancellation (ANC) system, the same microphone (located near the output transducer) can be used 2o for both the measurement of the signal to generate the "anti-noise" and the residual level measurement needed to provide the input level estimate required for the signal intelligibility enhancement processing. This combined approach works better than either method alone because ANC is limited to providing benefit at low frequencies (because of design considerations) and the signal 2s intelligibility enhancement provides benefit at higher frequencies. Using the same microphone reduces costs and simplifies the system. In many listening situations, low-frequency noise dominates. Here, the use of ANC at low frequencies to reduce the noise increases the available dynamic range, which results in improved fidelity relative to either method (ANC or SIE) being used alone.
5) Sometimes the signal-of-interest will contain noise. Using a psychoacoustic model andlor low-level expansion, the signal-of-interest can be processed such that the noise is placed below the acoustic signal level (or the residual signal level if ANC is being applied). When this is properly implemented, the listener perceives less noise.
6) It is also possible to incorporate single-microphone noise reduction techniques into the signal-of-interest channel [8]. This provides a signal for the listener that is more audible (relative to the environmental noise) and less tiring to listen to for extended periods of time because the processed signal-of-interest contains less noise.
7) When used with a Signal Activity Detector (SAD), an implementation is able to differentiate between a signal-of-interest and the environmental noise (interference). This ensures that the estimate of the noise signal does not become contaminated with the signal-of-interest, allowing voice communications to be clearer with higher intelligibility.
8) In an alternative realization, an adaptive filter is used to correlate the contaminated signal (signal + noise) with the uncontaminated electrical signal so 2o that an estimate of the noise can be derived. This provides a more reliable estimate of the noise signal that is contaminating the signal-of-interest.
Employing this technique provides improved signal fidelity.
9) In an alternative realization, a spectral differencing technique is used to estimate the spectral content of the environmental noise. This provides a more 25 reliable estimate of the noise signal that is contaminating the signal-of-interesting. This processing also improves signal fidelity.
10) With a multiband implementation of the compressor component (ranges of frequency are treated independently as opposed to compressing the entire spectrum uniformly) the overall perceived audio quality is improved [6].
Treating frequency bands independently of one another allows for greater freedom to produce high-fidelity compression. Furthermore, constraining the relative compression levels of the frequency ranges so a pre-determined maximum amount of frequency shaping may occur, maintains the signal quality across a wide range of noise environments. This ensures that frequency localized noise sources are better handled.
11 ) Using a multiband and/or adaptive level measurement of the noise allows an implementation to smoothly handle any changes of noise environment. It also protects against undesirable distortion, which would otherwise be caused by drastic changes in the environmental noise [5, 6].
12) A safety system is implicitly incorporated into the invention. The signal processing does not amplify desired sounds above the user's Loudness Discomfort Level (LDL). This is a safety feature designed to help protect the user's hearing in very high noise environments. It, along with the other ~5 adjustments provided by the invention, provide the opportunity to personalize an implementation to a specific user.
13) In summary, by using the invention, the user can receive a signal with improved SNR (signal-to-noise ratio) that continuously adapts to the user's environment, rendering the signal-of-interest at a comfortable level. This results 2o in improved signal intelligibility, improved perceived signal quality and less user fatigue.
A further understanding of the other features, aspects, and advantages of the present invention will be realized by reference to the following description, appended claims, and accompanying drawings.
2s Brief Description of the Drawings Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
Figure 1 illustrates a typical situation for a receive algorithm;
Figure 2 a schematic representation of a dynamic range mapping of signal-of-interest into available dynamic range;
Figure 3 shows a basic operation ofithe signal intelligibility enhancement according to the present invention;
Figure 4 shows a high-level block diagram of SIE processing according to the invention, incorporating a SAD;
Figure 5 shows a block diagram of SIE using adaptive noise estimation;
Figure 6 shows a block diagram of SIE using spectral differencing noise estimation;
Figure 7 show the input/gain function for straight-line compression;
Figure 8 shows one embodiment of the invention with SIE and ANC
combined;
Figure 9 is a diagram illustrating combining left and right noise floors;
Figure 10 shows a binaural combination system with transmit algorithm ~5 capability;
Figure 11 is a block diagram showing an open-loop SIE with shared TX
microphone; and Figure 12 is a block diagram showing an open-loop SIE with shared TX
microphones and directional processing.
20 [Note: In all the figures, a block labeled 'A' represents an analysis by a filterbank - preferably this is an oversampled WOLA filterbank, but this is not always a requirement. A block labeled 'S' represents a filterbank synthesis.
Again, it is preferable in most cases that this is an oversampled WOLA
filterbank, but this is not always necessary.]
25 Detailed Description of the Preferred Embodirnent(s) The preferred embodiment will be described with particular reference to a headset, to which the present invention is principally applied, but not exclusively.
Fig. 4 shows a block diagram of the invention.
Referring to Fig. 4, an acoustic input receives the environmental noise, possibly contaminated with the signal-of-interest or other signals (e.g., speech to be transmitted) from a microphone that is located either inside the ear canal (closed-loop implementation) or outside the ear canal (open-loop implementation).
In a closed loop implementation, equalization is included to account for ~o the acoustics of the signal path (e.g., an acoustic tube that supplies audio to a microphone molded into the earcup).
In an open loop implementation, a model of the transfer function from the microphone to the inside of the ear canal is incorporated to account for the attenuation and frequency response of the headset earcup and acoustic signal 15 path. A model of the output stage can also be included so that the level of the signal-of-interest that may appear in the ear canal, prior to any adaptive equalization, can be approximated.
In an open-loop implementation, a separate or shared environmental noise microphone can be used. In the shared microphone case, the same 2o microphone can be used for transmitting a signal (e.g., transmitted speech in a headset application). This reduces costs and simplifies mechanical construction.
In operation, the psychoacoustic model receives the level of the signal of interest in frequency bands or combinations of frequency bands (the desired signal spectrum) and using the level of environmental noise in frequency bands 2s or combinations frequency hands (the noise spectrum), computes dynamic range parameters that are applied by the multi-band compressor. The multi-band compressor then uses the dynamic range parameters supplied by the psychoacoustic model to equalize the signal as a function of frequency to improve its audibility. The use of a psychoacoustic model, combined with well-known dynamic range compression techniques, ensures that the output audio is made audible and intelligible over the environmental noise while minimizing perceived distortion and maintaining the quality of the desired signal.
Noise Estimation s An important input to the SIE signal processing is the spectrum of the environmental noise.
This can be supplied via a secondary input that receives the environmental noise by some method, such as a microphone. It can also be obtained by using a shared-input microphone (see below).
The SIE processing of the invention employs either a desired signal activity detector (DSAD), an adaptive estimation technique or a spectral differencing technique to obtain an accurate, uncontaminated estimate of the environmental noise spectrum.
The DSAD employs techniques well-known in the art to sample the spectrum of the signal when the desired signal is not present (i.e., during pauses or breaks in the desired signal). This ensures that the algorithm does not consider the desired signal (or in the case of a headset application with a shared microphone, the transmitted speech) to be part of the environmental noise.
In closed-loop implementations, when the DSAD indicates that there is no 2o desired signal present, the noise spectral image is updated. In an open-loop implementation, the DSAD may optionally monitor the environmental noise signal to ensure that transmitted speech or other signals of interest do not contaminate the noise spectrum that is supplied as an input to the psychoacoustic model.
2s If, in a closed-loop implementation, the noise spectrum has not been updated for some predetermined time period, the output audio may optionally mute for a brief period of time so that the noise spectrum can be updated without the desired signal being present. Using the DSAD in combination with timed updates (when necessary) ensures that noise spectrum is always current and that it is never contaminated with the desired signal spectrum.
Adaptive noise estimation employs techniques that are well-known in the art [11] to estimate the environmental noise. Fig. 5 shows a time domain technique, but it is understood that frequency domain techniques are also possible and rnay be advantageous. Thus, it is also possible to employ an oversampled, sub-band approach [see the co-pending patent application, which is filed on the same day by the present applicant entitled "Subband Adaptive Processing in an Oversampled Filterbank," and the disclosure of which is 1o incorporated herein by reference thereto.] Fig. 5 shows a block diagram of SIE
with Adaptive Noise Estimation.
Adaptive noise estimation requires no breaks in the desired signal to estimate the noise. The noise is continuously estimated using the correlation between the contaminated signal and the electrical signal. The adaptive correlator output contains primarily the signal components that are uncorrelated between the desired signal and the desired signal plus noise.
Spectral differencing takes the difference between a filtered or unfiltered version of the frequency domain representation of the signal-of-interest and the frequency domain representation of the environmental noise signal. This 2o subtraction can be done in bands or groups of bands. This estimation method is especially advantageous in closed-loop implementations (see below) where the environmental noise signal also contains the signal-of-interest because of the acoustic summation of the environmental noise and SIE processed signal-of-interest.
Filtering the signal-of-interest can be employed to derive a more accurate estimate. If the filter has a frequency response equivalent or approximately equivalent to the frequency response of.the output stage (SIE equalization, amplifier, loudspeaker and acoustics) and microphone, then the subtraction in the frequency domain provides an excellent approximation to the 3o uncontaminated (with the signal-of-interest) environmental noise.
Like adaptive estimation, spectral differencing requires no breaks in the desired signal to estimate the noise - the noise is continuously estimated using the spectral difference between the two signals (Fig. 6).
Psychoacoustic Processing s Four different strategies for the psychoacoustic model, and combinations thereof, can be employed to calculate the gains that are supplied as input to the multiband compressor. The gains are computed to ensure that the processed version of the desired signal is always audible over the environmental noise and that it is always comfortable for the listener. In all cases the LDL gives the upper limit of the dynamic range.
1 ) The lower limit of the dynamic range is set by the energy of the environmental noise within a frequency band or combination of bands.
2) The lower limit of the dynamic range is set by the level of the environmental noise within a frequency band or combination of bands, multiplied by a factor (X) between 0 and 1, which is adjustable. This factor controls the amount to which the apparatus amplifies low-level signals-of-interest. A lower X
results in more dynamic range being available for the signal-of-interest and improves signal quality. Too low an X will mean that at low-levels, the signal-of-interest is masked by the environmental noise.
results in more dynamic range being available for the signal-of-interest and improves signal quality. Too low an X will mean that at low-levels, the signal-of-interest is masked by the environmental noise.
3) The lower limit of the dynamic range is determined by a complex psychoacoustic model, well known in the prior art [10], which considers the level and shape of both the signal-of-interest and environmental noise to calculate the minimum audible and intelligible level within the noise.
4) The lower limit of the dynamic range is set by subtracting the SNR of the signal-of-interest (in decibels) from the energy of the noise within a channel.
Multi-band Compressor The dynamic range compression to a smaller effective dynamic range is accomplished by the use of one of several level mapping algorithms. These can be employed with the support of look-up tables or other well-known means to supply the shape of the compression Input vs. Gain Function, otherwise the gains can be directly calculated based on a mathematical formula. Three possible level-mapping algorithms are:
s 1 ) Straight-Line Compression - the InputIGain Function is a straight line (Fig. 8) then the level-mapping algorithm consists of a mathematical formula for the region of compression as expressed in decibels:
Gain = ENorse * (1 - Esignal LDL
2) Curvilinear compression - the InputIGain Function is not straight, but 1o curved to better fit growth-of-loudness perception in the human auditory system..
This method will yield improved perceptual fidelity but will either rely on a more complex formula or draw from a look-up table.
3) The psychoacoustic model is incorporated or integrated with the compressor to make the desired signal audible. The time variation of the gains is 15 controlled in such a way that perceptual distortion is minimized and the signal-of-interest is made as audible as possible.
A psychoacoustic model calculates a level to minimize the distortion in a given channel, by determining what sounds are audible within noise. This information leads to an objective estimation of the quality of the desired signal, 2o enabling the calculation of near-optimal compression parameters.
Other level mapping schemes are also possible.
It is often the case that the incoming signal-of-interest is not entirely noise-free. Instead of using compression on the entire dynamic range in this case, it is advantageous to expand (increase dynamic range) for the low-levels 2s of the signal where the noise exists. This effectively makes the noise quieter in the signal-of-interest and inaudible.
If the noise floor of the signal-of-interest is known, the dynamic range re-mapping shown in Fig. 2 reduces the audibility of this noise floor because it is masked by the environmental noise.
In order to deliver high perceptual fidelity in all environments, spectral tilt s constraints can be implemented. These constraints prevent the invention from over-processing the sound to the point where it the output audio is equalized in such a way that it is objectionable or quality is reduced in spectrally shaped noise environments. The constraints are implemented by enforcing a maximum gain difference between the various channels in the compressor. When the 1o invention attempts to exceed the maximum gain difference thresholds, a compromise is made in the more extreme channel and more or less gain is applied to satisfy the constraints.
Each individual is unique, and therefore each individual can determine and set his or her own LDL, desired listening level and growth of loudness. By 15 this process of personalization, key characteristics of operation are adjusted for the individual user (just like a hearing aid).
Combination with Active Noise Cancellation Many headsets today incorporate Active Noise Cancellation (ANC). ANC
technology is used to improve signal intelligibility in noisy environments by 2o generating anti-noise that actively cancels the environmental noise.
However, ANC is typically only effective for low frequencies because of well-known constraints of feedback systems. By incorporating the SIE invention with ANC
(Fig. 8), the audio quality and perceptibility is enhanced to a level that cannot be achieved by either method alone. Furthermore, an ANC system can have a 25 microphone already in place to sample the noise - this microphone can be simultaneously used for Signal Intelligibility Enhancement to sample the environmental noise in the ear canal. The combination of these two technologies will make it possible to make each one of them more subtle, and therefore less disorienting, while still delivering excellent quality and perceptibility.
A combination of SIE and ANC processing is preferably implemented using an oversampled WOLA filterbank as a pre-equalizer to an ANC system.
The ANC system can be implemented using analog or digital signal processing of a combination of these two. This processing is well-known in the art and is therefore not described. The WOLA measures that pre-equalized residual noise in the ear canal (closed loop ANC) or the outside environmental noise (open loop ANC) and uses this spectral information as input to a psychoacoustic model that provides dynamic range parameters for the pre-equalizer.
Binaural Operation 1o When used in a stereo audio system (e.g., binaural headset or in headphones), joint-channel processing extensions for SIE can be incorporated.
Two cases must be considered:
1 ) There is a microphone for each ear outside (open loop) or inside (closed loop) the earcup. In this case, the noise floor for the right and left sides 15 would be combined by some means (e.g., taking the maximum level or average of the left and right sides in each frequency channel) (Fig. 9).
2) There is only one microphone on one of the earcups or elsewhere on the apparatus. In this case, only one noise measurement is available.
Having only one noise measurement for the SIE algorithm is important 2o since a stereo compressor scheme (possibly with independent noise measurements) may lead to undesired independent channel adjustment and a consequent reduction in perceived audio quality.
When there is only one measure of the environmental noise for the user, both right and left sides of the SIE processing scheme use the same information. In the case of a stereo signal-of-interest, two SIE processing apparatus use the same environmental noise level on each audio stream.
In the case of a binaural headset and a monaural signal (e.g., cell phone headset - speech), only one SIE processing apparatus is implemented and the same electrical signal is sent to both output transducers (Fig. 10). This has the advantage of using only one D/A converter to deliver the processed signal out to the two output transducers.
Shared Noise Microphone In an open-loop configuration (typically used in telecommunications headset), a microphone used for the reception of transmitted (Tx) speech may also be used to sample the eravironmental noise (Fig. 11 ). This reduces cost and decreases hardware complexity. In the open-loop case it may be desirable to estimate the transfer function from the Tx microphone to the output transducer 1o to provide an estimate of the noise level in the ear can canal, this simulating the closed-loop condition.
Algorithms to restore the transmitted signal can also be incorporated with open-loop mic-sharing SIE (Fig. 11 ).
In Fig. 12, a well-known in the art or co-pending directional processing algorithm is used to noise-reduce the transmitted signal, but the same microphones that are used for the signal can be used to estimate the environmental noise employing the techniques descried for Fig. 11.
Either a DSAD, adaptive noise estimation or spectral differencing noise estimation can be used in any open-loop configuration.
2o While the present invention has been described with reference to specific embodiments, the description is illustrative of the invention and is not to be construed as limiting the invention. Various modifications may occur to those skilled in the art without departing from the true spirit and scope of the invention as defined by the appended claims.
List of References [1] Bose, Amar, et. al. Headphoning. United States Patent 4,455,675. Jun 19, 1984.
[2] Moy, Chu. Active Noise Reduction in Headphone Systems, Headwize Technical Paper Library, 1999. (http:l/headwize.com/tech/anr tech.htm) [3] Schneider & Brennan, Filterbank structure and method for filtering and separating an information signal info different bands, particularly for audio signal in hearing aids. United States Patent 6,236,731. April 16, 1998.
[4] Schneider & Brennan, Apparatus for and method of filtering in a digital 1 o hearing aid, including an application specific integrated circuif and a programmable digital signal processor. United States Patent 6,240,192. April 16, 1998.
Multi-band Compressor The dynamic range compression to a smaller effective dynamic range is accomplished by the use of one of several level mapping algorithms. These can be employed with the support of look-up tables or other well-known means to supply the shape of the compression Input vs. Gain Function, otherwise the gains can be directly calculated based on a mathematical formula. Three possible level-mapping algorithms are:
s 1 ) Straight-Line Compression - the InputIGain Function is a straight line (Fig. 8) then the level-mapping algorithm consists of a mathematical formula for the region of compression as expressed in decibels:
Gain = ENorse * (1 - Esignal LDL
2) Curvilinear compression - the InputIGain Function is not straight, but 1o curved to better fit growth-of-loudness perception in the human auditory system..
This method will yield improved perceptual fidelity but will either rely on a more complex formula or draw from a look-up table.
3) The psychoacoustic model is incorporated or integrated with the compressor to make the desired signal audible. The time variation of the gains is 15 controlled in such a way that perceptual distortion is minimized and the signal-of-interest is made as audible as possible.
A psychoacoustic model calculates a level to minimize the distortion in a given channel, by determining what sounds are audible within noise. This information leads to an objective estimation of the quality of the desired signal, 2o enabling the calculation of near-optimal compression parameters.
Other level mapping schemes are also possible.
It is often the case that the incoming signal-of-interest is not entirely noise-free. Instead of using compression on the entire dynamic range in this case, it is advantageous to expand (increase dynamic range) for the low-levels 2s of the signal where the noise exists. This effectively makes the noise quieter in the signal-of-interest and inaudible.
If the noise floor of the signal-of-interest is known, the dynamic range re-mapping shown in Fig. 2 reduces the audibility of this noise floor because it is masked by the environmental noise.
In order to deliver high perceptual fidelity in all environments, spectral tilt s constraints can be implemented. These constraints prevent the invention from over-processing the sound to the point where it the output audio is equalized in such a way that it is objectionable or quality is reduced in spectrally shaped noise environments. The constraints are implemented by enforcing a maximum gain difference between the various channels in the compressor. When the 1o invention attempts to exceed the maximum gain difference thresholds, a compromise is made in the more extreme channel and more or less gain is applied to satisfy the constraints.
Each individual is unique, and therefore each individual can determine and set his or her own LDL, desired listening level and growth of loudness. By 15 this process of personalization, key characteristics of operation are adjusted for the individual user (just like a hearing aid).
Combination with Active Noise Cancellation Many headsets today incorporate Active Noise Cancellation (ANC). ANC
technology is used to improve signal intelligibility in noisy environments by 2o generating anti-noise that actively cancels the environmental noise.
However, ANC is typically only effective for low frequencies because of well-known constraints of feedback systems. By incorporating the SIE invention with ANC
(Fig. 8), the audio quality and perceptibility is enhanced to a level that cannot be achieved by either method alone. Furthermore, an ANC system can have a 25 microphone already in place to sample the noise - this microphone can be simultaneously used for Signal Intelligibility Enhancement to sample the environmental noise in the ear canal. The combination of these two technologies will make it possible to make each one of them more subtle, and therefore less disorienting, while still delivering excellent quality and perceptibility.
A combination of SIE and ANC processing is preferably implemented using an oversampled WOLA filterbank as a pre-equalizer to an ANC system.
The ANC system can be implemented using analog or digital signal processing of a combination of these two. This processing is well-known in the art and is therefore not described. The WOLA measures that pre-equalized residual noise in the ear canal (closed loop ANC) or the outside environmental noise (open loop ANC) and uses this spectral information as input to a psychoacoustic model that provides dynamic range parameters for the pre-equalizer.
Binaural Operation 1o When used in a stereo audio system (e.g., binaural headset or in headphones), joint-channel processing extensions for SIE can be incorporated.
Two cases must be considered:
1 ) There is a microphone for each ear outside (open loop) or inside (closed loop) the earcup. In this case, the noise floor for the right and left sides 15 would be combined by some means (e.g., taking the maximum level or average of the left and right sides in each frequency channel) (Fig. 9).
2) There is only one microphone on one of the earcups or elsewhere on the apparatus. In this case, only one noise measurement is available.
Having only one noise measurement for the SIE algorithm is important 2o since a stereo compressor scheme (possibly with independent noise measurements) may lead to undesired independent channel adjustment and a consequent reduction in perceived audio quality.
When there is only one measure of the environmental noise for the user, both right and left sides of the SIE processing scheme use the same information. In the case of a stereo signal-of-interest, two SIE processing apparatus use the same environmental noise level on each audio stream.
In the case of a binaural headset and a monaural signal (e.g., cell phone headset - speech), only one SIE processing apparatus is implemented and the same electrical signal is sent to both output transducers (Fig. 10). This has the advantage of using only one D/A converter to deliver the processed signal out to the two output transducers.
Shared Noise Microphone In an open-loop configuration (typically used in telecommunications headset), a microphone used for the reception of transmitted (Tx) speech may also be used to sample the eravironmental noise (Fig. 11 ). This reduces cost and decreases hardware complexity. In the open-loop case it may be desirable to estimate the transfer function from the Tx microphone to the output transducer 1o to provide an estimate of the noise level in the ear can canal, this simulating the closed-loop condition.
Algorithms to restore the transmitted signal can also be incorporated with open-loop mic-sharing SIE (Fig. 11 ).
In Fig. 12, a well-known in the art or co-pending directional processing algorithm is used to noise-reduce the transmitted signal, but the same microphones that are used for the signal can be used to estimate the environmental noise employing the techniques descried for Fig. 11.
Either a DSAD, adaptive noise estimation or spectral differencing noise estimation can be used in any open-loop configuration.
2o While the present invention has been described with reference to specific embodiments, the description is illustrative of the invention and is not to be construed as limiting the invention. Various modifications may occur to those skilled in the art without departing from the true spirit and scope of the invention as defined by the appended claims.
List of References [1] Bose, Amar, et. al. Headphoning. United States Patent 4,455,675. Jun 19, 1984.
[2] Moy, Chu. Active Noise Reduction in Headphone Systems, Headwize Technical Paper Library, 1999. (http:l/headwize.com/tech/anr tech.htm) [3] Schneider & Brennan, Filterbank structure and method for filtering and separating an information signal info different bands, particularly for audio signal in hearing aids. United States Patent 6,236,731. April 16, 1998.
[4] Schneider & Brennan, Apparatus for and method of filtering in a digital 1 o hearing aid, including an application specific integrated circuif and a programmable digital signal processor. United States Patent 6,240,192. April 16, 1998.
[5] Schneider, Todd A. An Adaptive Dynamic Range Controller, MASc Thesis, University of Waterloo, Waterloo, Ontario, Canada. 1991.
[6] Schneider & Brennan. A Compression Strategy for a Digital Hearing Aid, Proc. ICASSP 1997, Munich, Germany.
[7] http:/Iwww.its.bldrdoc.govlprojectslt1 glossary2000/ mu-law algorithm.html [8] Brennan, Robert. Method and.Apparatus for Noise Reduction, 2o Particularly in Hearing Aids, PCTICanadian Patent Application PCT/CA98100331.
[9] Schmidt, John. Apparatus for Dynamic Range Compression of an Audio Signal, US Patent 5,832,444.
[10] K. Brandenburg and G. Stoll, "lS0-MPEG9 Audio: A Generic 2s Standard for Coding of High-Quality Digifal Audio," Journal of the Audio Engineering Society, vol. 42, pp. 780--792, Oct. 1994.
[11] Widrow, B. and Stearns, S.D. Adaptive Signal Processing. Prentice-Hall, Englewood Cliffs, NJ.
Claims (12)
1. A sound intelligibility enhancement system using a psychoacoustic model, wherein the level of the signal-of-interest that falls below the environmental noise is selectively amplified as a function of the masking calculated by the psychoacoustic model so that it is audible above the noise.
2. A system as in claim 1, wherein the processing is done in an oversampled filterbank.
3. A system as in claim 1, wherein the noise is estimated during breaks in the signal-of-interest using a signal activity detector.
4. A system as in claim 1, wherein the noise is estimated using an adaptive estimate technique.
5. A system as in claim 1, wherein the noise is estimated using a spectral subtraction technique.
6. A system as in claim 1, wherein the same microphone is used to derive the environmental noise and also to capture a signal in the noise to be transmitted.
7. A system as in claim 1, further comprising a plurality of microphones to derive the environmental noise, wherein the signals from all microphones are combined to form a single representative noise estimate.
8. A system as in claim 1, wherein the microphone to detect the environmental noise is positioned such that it also detects the signal-of-interest.
9. A system as in claim 1, wherein the microphone to detect the environmental noise is positioned such that it does not detect the signal-of-interest.
10. A system as in claim 1, wherein the output signal is sent to a plurality of transducers.
11. A sound intelligibility enhancement system using a psychoacoustic model, which also incorporates active noise cancellation, wherein the level of the signal-of-interest that falls below the environmental noise is selectively amplified as a function of the masking calculated by the psychoacoustic model so that it is audible above the noise floor.
12. A system as in claim 11, wherein the same microphone is used for both active noise cancellation and the sound intelligibility processing.
Priority Applications (13)
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| CA002354755A CA2354755A1 (en) | 2001-08-07 | 2001-08-07 | Sound intelligibilty enhancement using a psychoacoustic model and an oversampled filterbank |
| CNB028177452A CN1308915C (en) | 2001-08-07 | 2002-08-07 | Sound intelligibilty enhancement using a psychoacoustic model and an oversampled filterbank |
| EP02754004A EP1417679B1 (en) | 2001-08-07 | 2002-08-07 | Sound intelligibility enhancement using a psychoacoustic model and an oversampled filterbank |
| CN200710006509.7A CN101105941B (en) | 2001-08-07 | 2002-08-07 | System for enhancing sound definition |
| DE60238619T DE60238619D1 (en) | 2001-08-07 | 2002-08-07 | IMPROVEMENT OF LANGUAGE COMPATIBILITY WITH A PSYCHOA ACOUSTIC MODEL AND A FILTER BANK CHANGED |
| JP2003519932A JP4731115B2 (en) | 2001-08-07 | 2002-08-07 | Improvement of speech intelligibility using psychoacoustic model and oversampled filter bank |
| CA002397084A CA2397084C (en) | 2001-08-07 | 2002-08-07 | Sound intelligibilty enhancement using a psychoacoustic model and an oversampled filterbank |
| AU2002322866A AU2002322866B2 (en) | 2001-08-07 | 2002-08-07 | Sound intelligibility enhancement using a psychoacoustic model and an oversampled filterbank |
| DK02754004.6T DK1417679T3 (en) | 2001-08-07 | 2002-08-07 | Improving sound clarity using a psychoacoustic model and an oversampled filter bank |
| US10/214,056 US7050966B2 (en) | 2001-08-07 | 2002-08-07 | Sound intelligibility enhancement using a psychoacoustic model and an oversampled filterbank |
| PCT/CA2002/001221 WO2003015082A1 (en) | 2001-08-07 | 2002-08-07 | Sound intelligibilty enchancement using a psychoacoustic model and an oversampled fiolterbank |
| AT02754004T ATE492015T1 (en) | 2001-08-07 | 2002-08-07 | IMPROVING SPEECH UNDERSTANDABILITY USING A PSYCHOACOUSTIC MODEL AND AN OVERSAMPLED FILTER BANK |
| JP2010094838A JP2010200350A (en) | 2001-08-07 | 2010-04-16 | Sound intelligibility enhancement using psychoacoustic model and oversampled filterbank |
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| CA002354755A CA2354755A1 (en) | 2001-08-07 | 2001-08-07 | Sound intelligibilty enhancement using a psychoacoustic model and an oversampled filterbank |
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| EP (1) | EP1417679B1 (en) |
| JP (2) | JP4731115B2 (en) |
| CN (2) | CN1308915C (en) |
| AT (1) | ATE492015T1 (en) |
| AU (1) | AU2002322866B2 (en) |
| CA (1) | CA2354755A1 (en) |
| DE (1) | DE60238619D1 (en) |
| DK (1) | DK1417679T3 (en) |
| WO (1) | WO2003015082A1 (en) |
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- 2002-08-07 DK DK02754004.6T patent/DK1417679T3/en active
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- 2002-08-07 AU AU2002322866A patent/AU2002322866B2/en not_active Ceased
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2003015464A2 (en) | 2001-08-08 | 2003-02-20 | Dspfactory Ltd | Directional audio signal processing using an oversampled filterbank |
| US7359520B2 (en) | 2001-08-08 | 2008-04-15 | Dspfactory Ltd. | Directional audio signal processing using an oversampled filterbank |
| CN110875055A (en) * | 2018-08-29 | 2020-03-10 | 国光电器股份有限公司 | Multi-band audio signal dynamic range compression with overshoot suppression |
| CN112822592A (en) * | 2020-12-31 | 2021-05-18 | 青岛理工大学 | Active noise reduction earphone capable of directionally listening and control method |
| CN112822592B (en) * | 2020-12-31 | 2022-07-12 | 青岛理工大学 | Active noise reduction earphone capable of directionally listening and control method |
Also Published As
| Publication number | Publication date |
|---|---|
| EP1417679A1 (en) | 2004-05-12 |
| DE60238619D1 (en) | 2011-01-27 |
| CN1308915C (en) | 2007-04-04 |
| DK1417679T3 (en) | 2011-03-28 |
| JP4731115B2 (en) | 2011-07-20 |
| ATE492015T1 (en) | 2011-01-15 |
| US7050966B2 (en) | 2006-05-23 |
| AU2002322866B2 (en) | 2007-10-11 |
| CN1568502A (en) | 2005-01-19 |
| JP2004537940A (en) | 2004-12-16 |
| CN101105941B (en) | 2010-09-22 |
| US20030198357A1 (en) | 2003-10-23 |
| CN101105941A (en) | 2008-01-16 |
| EP1417679B1 (en) | 2010-12-15 |
| WO2003015082A1 (en) | 2003-02-20 |
| JP2010200350A (en) | 2010-09-09 |
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