AU7826901A - Apparatus for noise reduction, particulary in hearing aids - Google Patents

Description

Regulation 3.2

AUSTRALIA

Patents Act 1990 COMPLETE SPECIFICATION FOR A STANDARD PATENT

(ORIGINAL)

*oo a Name of Applicant: Actual Inventor: Address for Service: Invention Title: DSPFACTORY LTD., of Waterloo, Ontario N2J 1P5, ROBERT BRENNAN DAVIES COLLISON CAVE, Patent Attorneys, of 1 Little Collins Street, Melbourne, Victoria 3000, Australia Apparatus For Noise Reduction, Particularly In Hearing Aids 80 King Street South, Suite 206, Canada ooeee ooee eoooo The following statement is a full description of this invention, including the best method of performing it known to us: -1 Q:\oper\gcp\69155div c.doc 8/10/01 APPARATUS FOR NOISE REDUCTION, PARTICULARLY IN HEARING AIDS FIELD OF THE INVENTION This invention relates to noise reduction in audio or other signals and more particularly relates to noise reduction in digital hearing aids.

BACKGROUND OF THE INVENTION Under noisy conditions, hearing impaired persons are severely disadvantaged compared to those with normal hearing. As a result of reduced cochlea processing, hearing impaired persons are typically much less able to distinguish between meaningful speech and competing sound sources noise). The increased attention necessary for understanding of speech quickly leads to listener fatigue. Unfortunately, conventional hearing aids do little to aid this problem since both speech and noise are 0. 0 15 boosted by the same amount.

In Sheikhzadeh et al., "Comparative Performance of Spectral Subtraction and HMM Based Speech Enhancement Strategies with Application to Hearing Aid Design", Proceedings of the International Conference on Acoustics, Speech, Signal Processing (ICASSP), Vol. 1, pp. I- 20 13 to 1-16 (April 19, 1994, IEEE), a basic spectral subtraction noise suppression approach and various HMM noise reduction approaches are generally described. The basic spectral subtraction or Wiener filtering approach oooo• S•theoretically minimizes noise power relative to speech. This noise suppression method includes performing an FFT on each frame of the 25 input noisy signal to estimate the noisy speech spectrum, An estimate of the noise spectrum is updated during periods of non-speech activity with the aid of an autocorrelation-based voicing and pitch detector when no speech is detected the signal is assumed to be noise). A frequency domain Wiener filter is constructed from the speech and noise spectral estimates and is used to obtain a noise reduced or enhanced signal (after an inverse P:IOPER\GCP\(,69 I55div spe.doc-)8lO/I -3- FFT transformation). However, this and the other approaches described in the Sheikhzadeh et al. reference may, in practice, still result in unacceptable levels of noise in the output signal. Furthermore, these approaches also suffer from the musical noise phenomenon as well as from degradation in the perceptual quality of the noise reduced signal.

In addition, compression algorithms used in some hearing aids boost low level signals to a greater extent than high level signals. This works well with low noise signals by raising low level speech cues to audibility. A disadvantage associated with prior art hearing aid systems that process inputs using both noise suppression and signal compression is that, at high noise levels, compression performs only modestly since the action of the compressor is unduly influenced by the noise and merely boosts the noise floor. For persons that frequently work in high ambient sound environments, this can lead to unacceptable results.

15 BRIEF SUMMARY OF THE INVENTION According to the present invention there is provided an apparatus for reducing noise in an input signal, the apparatus including an input for receiving the input signal, the apparatus comprising: a main noise reduction unit connected to the input for generating a noise reduced signal; a compression circuit for receiving a compression control signal i and generating an amplification control signal in response; 'o an amplification unit for receiving the noise reduced signal and the amplification control signal and generating an output signal with compression and reduced noise; characterised in that the apparatus further comprises: an auxiliary noise reduction unit connected to the input for generating an auxiliary noise reduced signal, the compression control signal being the auxiliary noise reduced signal.

P.\OPER\GCP69I155div sp .doc-4lO8I00[ -4- Preferably, the input signal contains speech and the main noise reduction unit comprises a detector connected to said input and providing a detection signal indicative of the presence of speech; magnitude means for determining the magnitude spectrum of the input signal with both the detector and the magnitude means being connected to the input of the apparatus; spectral estimate means for generating a noise magnitude spectral estimate and being connected to the detector and to the input of the apparatus; a noise filter calculation unit connected to the spectral estimate means and the magnitude means, for receiving the noise magnitude spectral estimate and magnitude spectrum of the input signal (IX(f)j) and calculating an attenuation function and a multiplication unit coupled to the noise filter calculation unit and the input signal for producing the noise reduced signal.

S"BRIEF DESCRIPTION OF THE DRAWING FIGURES o For a better understanding of the present invention and to show more o 15 clearly how it may be carried into effect, reference will now be made, by way of example, to the accompanying drawings in which: Figure 1 is a conceptual blocked diagram for hearing aid noise reduction and compression; Figure 2 shows a detailed blocked diagram for noise reduction in a hearing aid; Figure 3 shows a modified auto-correlation scheme performed in segments.

DESCRIPTION OF THE PREFERRED EMBODIMENT Referring first to Figure 1, there is shown schematically a basic strategy employed by the present invention. An input 10 for a noisy signal is split into two paths 12 and 14. In the upper path 12, the noise reduction is effected as indicated in block 16. In the lower path 14, noise reduction is effected in unit 18. The noise reduction unit 18 provides a cleaner signal that is supplied to compression circuitry 5 and the compression circuitry controls amplification unit 22 amplifying the signal in the upper path to generate an output signal at 24.

Here, the poi-flon of the noise -dtion unit 18 can advantageously provide a cleaner signal for controlling the compression stage. The noise reduction unit 18 provides a first generating means which generates an auxiliary signal from an auxiliary noise reduction algorithm.

The auxiliary algorithm performed by unit 18 may be identical to the one performed by unit 16, except with different parameters. Since the auxiliary noise reduced signal is not heard, unit 18 can reduce noise with increased aggression. This auxiliary signal, in turn, controls the compression circuitry which comprises second generating means for generating a control input for controlling the amplification unit 22.

The noise reduction unit 16 can be effected by using a different noise reduction algorithm from that in the noise reduction unit 18. If the same algorithm is used for both noise reduction processes 16 and 18, then the two paths can be merged prior to being split up to go to units and 22.

o With reference to Figure 2, this shows a block diagram of a specific realization of the proposed noise reduction technique which is preferably carried out by noise reduction unit 16 (and possibly also noise reduction unit 18). The incoming signal at 10 is first blocked and windowed, as detailed in applicants' simultaneously filed international application no.

PCT/CA98/00331 corresponding to international publication no. WO 98/47313. The blocked and windowed output provides the input to the frequency transform (all of these steps take place, as indicated, at 32), which -preferably here is a Discrete Fourier Transform (DFT), to provide a signal The present invention is not however restricted to a DFT and other transforms can be used. A known, fast way of implementing a DFT with mild restrictions on the transform size is the Fast Fourier Transform (FFT).

The input 10 is also connected to a speech detector 34 which works in parallel to isolate the pauses in the incoming speech. For simplicity, 6 reference is made here to "speech", but it will be understood that this encompasses any desired audio signal capable of being isolated or detected by detector 34. These pauses provide opportunities to update the noise spectral estimate. This estimate is updated only during speech pauses as a running slow average. When speech is detected, the noise estimate is frozen.

As indicated at 38, the outputs from both the unit 32 and the voice detection unit 34 are connected to block 38 which detects the magnitude spectrum of the incoming noise, Ii(f)l. The magnitude spectrum detected by unit 38 is an estimate. The output of unit 32 is also connected to block 36 for detecting the magnitude spectrum of the incoming noisy signal, IX(f)l.

A noise filter calculation 40 is made based on IX(f)l and iN to calculate an attenuation function As indicated at 42, this is used to control the original noisy signal X(f) by multiplying X(f) by H(f).

This signal is subject to an inverse transform and overlap-add resynthesis in known manner at 44, to provide a noise reduced signal (which may be the signal at 12 or 14 in Figure 1).

During speech utterances, the magnitude spectrum is compared with the noise spectral estimate. In general, frequency dependent attenuation is calculated as a function of the two input spectra. Frequency regions where the incoming signal is higher than the noise are attenuated less than regions where the incoming signal is comparable or less than the .e -7noise. The attenuation function is generally given by ISV) a S IS(f)12 IN(/f)1 2 where H(f) is the attenuation as a function of frequency S(f) is the clean speech spectrum N(f) is the noise spectrum a is the attenuation rule The attenuation rule preferably selected is the Wiener attenuation rule which corresponds to a equal to 1. The Wiener rule minimizes the noise power relative to the speech. Other attenuation rules can also be used, for example the spectral subtraction rule having a equal to Since neither S(f) nor N(f) are precisely known and would require a priori knowledge of the clean speech and noise spectra, they are replaced by estimates and 1(f): 1 sv, =2 X(f) 1 (f 2 where X(f) is the incoming speech spectrum and 1(f) is the noise spectrum 15 as estimated during speech pauses. Given perfect estimates of the speech and noise spectra, application of this formula yields the optimum (largest) signal-to-noise-ratio (SNR). Although the SNR would be maximized using this formula, the noise in the resulting speech is still judged as excessive by subjective assessment. An improved implementation of the formula taking 20 into account these perceptual aspects is given by: 2 I 2'-P ff) 2

H

w i-xOT l where: 0 is an oversubtraction factor -8a is the attenuation rule H(f) should be between 0.0 and 1.0 to be meaningful. When negative results are obtained, H(f) is simply set to zero at that frequency. In addition, it is beneficial to increase the minimum value of H(f) somewhat above zero to avoid complete suppression of the noise. While counterintuitive, this reduces the musical noise artifact (discussed later) to some extent. The parameter a governs the attenuation rule for increasing noise levels. Generally, the higher a is set, the more the noise is punished as X(f) drops. It was found that the best perceptual results were obtained with a 1.0. The special case of a 1.0 and 3=1.0 corresponds to power spectrum subtraction yielding the Wiener filter solution as described above.

The parameter P controls the amount of additional noise suppression required; it is ideally a function of the input noise level.

Empirically it was noticed that under very light noise (SNR 40 dB) P 15 should be zero. For lower SNR signals, the noise reduction becomes less reliable and is gradually turned off. An example of this additional noise reduction is: for SNR<0

SNR

IP=P0 5 for 20 P=Po[l- S for 5<SNR<40 3=0 for In this example, po refers to the maximum attenuation, 5.0. In effect, from SNR 0, the attenuation P is ramped up uniformly to a maximum, 0o, at SNR 5, and this is then uniformly ramped down to zero at SNR Another aspect of the present invention provides improvements in perceptual quality making P a function of frequency. As -9an instance of the use of this feature, it was found that to avoid excessive attenuation of high frequency information, it was necessary to apply a preemphasis function, to the input spectrum X(f) where P(f) is an increasing function of frequency. The effect of this preemphasis function is to artificially raise the input spectrum above the noise floor at high frequencies. The attenuation rule will then leave the higher frequencies relatively intact. This preemphasis is conveniently accomplished by reducing 3 at high frequencies by the preemphasis factor.

P where 0 is p after preemphasis.

PW Without further modification, the above formula can yield noise reduced speech with an audible artifact known as musical noise. This occurs, because in order for the noise reduction to be effective in reducing noise, the frequency attenuation function has to be adaptive. The very act of adapting this filter allows isolated frequency regions of low SNR to flicker 15 in and out of audibility leading to this musical noise artifact. Various :methods are used to reduce this problem. Slowing down the adaptation rate significantly reduces this problem. In this method, a forgetting factor, y is introduced to slow abrupt gain changes in the attenuation function: where Gn(f) and Gn- 1 are the smoothed attenuation functions at the n'th 20 and (n-1)'th time frames.

Further improvements in perceptual quality are possible by making 3 (in addition to being a function of frequency) a function of perceptual distortion. In this method, the smoothing function (instead of a simple exponential or forgetting factor as above) bases its decision on adapting Gn(f) on whether such a change is masked perceptually. The perceptual adaptation algorithm uses the ideal attenuation function H(f) as a target because it represents the best SNR attainable. The algorithm decides how much Gn(f) can be adjusted while minimizing the perceptual distortion. The decision is based on a number of masking criteria in the output spectrum including: 1. Spread of masking changes in higher frequency energy are masked by the presence of energy in frequencies in the vicinity especially lower frequencies; 2. Previous energy changes in louder frequency components are more audible that changes in weaker frequency components; 3. Threshold of hearing there is no point in reducing the noise significantly below the threshold of hearing at a particular frequency; 4. Previous attenuation low levels should not be allowed to jump up rapidly high levels should not suddenly drop rapidly unless masked by 2) or 3).

For applications where the noise reduction is used to ~preprocess the input signal before reaching the compression circuitry (schematically shown in Figure the perceptual characteristics of the noise reduced signal are less important. In fact, it may prove advantageous to perform the noise reduction with two different suppression algorithms as *o e20 mentioned above. The noise reduction 16 would be optimized for perceptual quality while the other noise reduction 18 would be optimized for good compression performance.

A key element to the success of the present noise suppression or reduction system is the speech or voicing detector. It is crucial to obtain accurate estimates of the noise spectrum. If the noise *....spectral estimate is updated during periods of speech activity, the noise ::spectrum will be contaminated with speech resulting in speech cancellation.

Speech detection is very difficult, especially under heavy noise situationsq: Although, a three-way distinction between voiced speech, unvoiced speech (consonants) and noise is possible under light noise conditions, it was found that the only reliable distinction available in heavy noise was between voiced speech and noise. Given the slow averaging of the noise -11spectrum, the addition of low-energy consonants is insignificant.

Thus, another aspect of the present invention uses an auto-correlation function to detect speech, as the advantage of this function is the relative ease with which a periodic signal is detected. As will be appreciated by those skilled in the art, an inherent property of the autocorrelation function of a periodic signal is that it shows a peak at the time lag corresponding to the repetition period (see Rabiner, and Schafer, Digital Processing of Speech Signals, (Prentice Hall Inc., 1978)). Since voiced speech is nearly periodic in time at the rate of its pitch period, a voicing detector based on the auto-correlation function was developed.

Given a sufficiently long auto-correlation, the uncorrelated noise tends to cancel out as successive pitch periods are averaged together.

A strict short-time auto-correlation requires that the signal first be blocked to limit the time extent (samples outside the block are set to zero). This operation is followed by an auto-correlation on the block. The disadvantage of this approach is that the auto-correlation function includes fewer samples as the time lag increases. Since the pitch lag (typically between 40 and 240 samples (equivalent to 2.5 to 15 milliseconds) is a significant portion of the auto-correlation frame (typically 512 samples or 32 milliseconds), a modified version of the auto-correlation function avoiding this problem was calculated. This modified version of the auto-correlation function is described in Rabiner, and Schafer, Digital Processing of Speech Signals, supra. In this method, the signal is blocked and correlated with a delayed block (of the same length) of the signal. Since the samples in the delayed block include samples not present in the first block, this *.function is not a strict auto-correlation but shows periodicities better.

::It is realized that a hearing aid is a real-time system and that all computational elements for each speech block are to be completed before the next arrives. The calculation time of a long auto-correlation, which is required only every few speech blocks, would certainly bring the system to a halt every time it must be calculated. It is therefore recognized -12that the auto-correlation should be segmented into a number of shorter sections which can be calculated for each block and stored in a partial correlation table. The complete auto-correlation is determined by stacking these partial correlations on top of each other and adding as shown in Figure 3.

Referring to Figure 3, input sample 50 is divided into separate blocks stored in memory buffers as indicated at 52. The correlation buffers 52 are connected to a block correlation unit 54, where the autocorrelation is performed. Partial cross-correlations 56 are summed to give the final correlation 58.

This technique quickly yields the exact modified autocorrelation and is the preferred embodiment when sufficient memory is available to store the partial correlations.

When memory space considerations rule out the above technique, a form of exponential averaging may be used to reduce the number of correlation buffers to a single buffer. In this technique, successive partial correlations are summed to the scaled down previous .o contents of the correlation buffer. This simplification significantly reduces the memory but implicitly applies an exponential window to the input sequence. The windowing action, unfortunately, reduces time periodicities.

The effect is to spread the autocorrelation peak to a number of adjacent time lags in either direction. This peak smearing reduces the accuracy of the voicing detection somewhat.

In the implementations using an FFT transform block, these partial correlations (for either technique given above) can be performed quickly in the frequency domain. For each block, the correlation operation is reduced to a sequence of complex multiplications on the transformed time sequences. The resulting frequency domain sequences can be added directly together and transformed back to the time domain to provide the complete long auto-correlation. In an alternate embodiment, the frequency domain correlation results are never inverted back to the time domain. In this realization, the pitch frequency is determined directly P:\OPER\GCPv,9155di sp.doc-Si/Ol 13in frequency domain.

Since the auto-correlation frame is long compared to the (shorter) speech frame, the voicing detection is delayed compared to the current frame. This compensation for this delay is accomplished in the noise spectrum update block.

An inter-frame constraint was placed on frames considered as potential candidates for speech pauses to further reduce false detection of noise frames. The spectral distance between the proposed frame and the previous estimates of the noise spectrum are compared. Large values reduce the likelihood that the frame is truly a pause. The voicing detector takes this information, the presence or absence of an autocorrelation peak, the frame energy, and a running average of the noise as inputs.

The reference to any prior art in this specification is not, and should not be taken as, an acknowledgment or any form of suggestion that that prior art forms part of the common general knowledge in Australia.

Throughout this specification and the claims which follow, unless the 15 context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or Io. group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

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Claims (6)

1. An apparatus for reducing noise in an input signal, the apparatus including an input for receiving the input signal, the apparatus comprising: a main noise reduction unit connected to the input for generating a noise reduced signal; a compression circuit for receiving a compression control signal and generating an amplification control signal in response; an amplification unit for receiving the noise reduced signal and the amplification control signal and generating an output signal with compression and reduced noise; characterised in that the apparatus further comprises: an auxiliary noise reduction unit connected to the input for generating an auxiliary noise reduced signal, the compression control signal being the 15 auxiliary noise reduced signal.

2. An apparatus as claimed in claim 1 wherein the input signal contains speech and the main noise reduction unit comprises: a detector connected to said input and providing a detection signal indicative of the presence of speech; magnitude means for determining the magnitude spectrum of the input ~signal with both the detector and the magnitude means being connected to the input of the apparatus; spectral estimate means for generating a noise magnitude spectral estimate and being connected to the detector and to the input of the apparatus; a noise filter calculation unit connected to the spectral estimate means and the magnitude means, for receiving the noise magnitude spectral estimate P:\OPER\GCP\691 55div sp.doc-8/10/l01 and magnitude spectrum of the input signal (IX(f)j) and calculating an attenuation function and a multiplication unit coupled to the noise filter calculation unit and the input signal for producing the noise reduced signal.

3. An apparatus as claimed in claim 2 wherein the main noise reduction unit and the auxiliary noise unit comprise a single unit.

4. An apparatus as claimed in claim 2 wherein the auxiliary noise reduction unit is different from the main noise reduction unit. An apparatus as claimed in claim 2 wherein the input signal has a signal to noise ,0:,ratio and the noise filter calculation unit produces the noise reduced signal in 0*° dependence upon the signal to noise ratio, wherein there is no substantial modification 15 to the input signal for very low and for very high signal to noise ratios. S6. An apparatus as claimed in claim 2 or 5, which includes a frequency transform means connected between said input and both of the magnitude means and the spectral estimate means for transforming the signal into the frequency domain to provide a transformed signal wherein the magnitude means determines the magnitude V spectrum from the transformed signal and wherein the spectral estimate means determines the noise spectral estimate from the transformed signal in the absence of speech, the apparatus further including inverse frequency transform means for receiving a transformed noise reduced signal from the multiplication unit, the inverse frequency transform means providing the noise reduced signal. P:\OPER\GCP,69155dl spe.doc-l8//lll I -16-

7. An apparatus as claimed in claim 6, wherein the noise filter calculation means determines the square of the speech magnitude spectral estimate by subtracting the square of the noise magnitude spectral estimate from the square of the magnitude spectrum of the input signal and wherein the noise filter calculation means calculates the attenuation function as a function of frequency, in accordance with the following equation: IX(f) 2 PN(f) 2 2 where denotes frequency, H(f) is the attenuation function, is the magnitude spectrum of the input audio signal; N(f) is the noise magnitude spectral estimate, j is an oversubtraction factor and a is an attenuation rule, wherein a and f are selected to give a desired attenuation function.

8. An apparatus for reducing noise in an input signal substantially as hereinbefore described with reference to the accompanying drawings. *****DATED this 8th day of October, 2001 DAVIES COLLISON CAVE DATED this 8th day of October, 2001 DSPFACTORY LTD. By its Patent Attorneys DAVIES COLLISON CAVE