Noise suppression system - Patent Review 4811404

Claims

What is claimed is:

1. An improved noise suppression system for attenuating the background noise from a noisy input signal to produce a noise-suppressed output signal, said noise suppression system comprising:

means for separating the input signal into a plurality of pre-processed signals representative of selected frequency channels;

means for generating estimates of the signal-plus-noise energy and the noise energy in each individual channel;

means for producing a gain value for each individual channel in response to said channel energy estimates, said gain values having a minimum gain value for each channel, said gain value producing means including threshold means for allowing gain values above said minimum gain value to be prodeced only when said signal-plus-noise energy estimates exceed said noise energy estimates by a predetermined amount; and

means for modifying the gain of each of said plurality of pre-processed signals in esponse to said gain values to provide a plurality of post-processed signals.

2. The noise suppression system according to claim 1, wherein said gain value producing means produces gain values based upon the signal-to-noise ratio (SNR) of said channel energy estimates, and wherein said SNR estimates are compared with a predefined SNR threshold such that channels having SNR estimates below said SNR threshold produce minimum gain values.

3. The noise suppression system according to claim 2, wherein said predefined SNR threshold corresponds to an SNR value within the range of 1.5 dB to 5 dB SNR.

4. The noise suppression system according to claim 3, wherein said predefined SNR threshold corresponds to an SNR value of approximately 2.25 dB SNR.

5. The noise suppression system according to claim 1, wherein said gain modifying means provides a maximum amount of attenuation of the pre-processed signal in a particular channel having a minimum gain value.

6. The noise suppression system according to claim 1, wherein gain values produce a higher amount of attenuation for high frequency channels than low frequency channels.

7. The noise suppression system according to claim 1, wherein said gain value producing means further includes a plurality of gain tables, each gain table having predetermined individual channel gain values corresponding to said individual channel energy estimates, and gain table selection means for automatically selecting one of said plurality of gain tables as a function of the overall average background noise level of said input signal.

8. The noise suppression system according to claim 1, further includes means for combining said plurality of post-processed signals to produce said noise-suppressed output signal.

9. An improved noise suppression system for attenuating the background noise from a noisy input signal to produce a noise-suppressed output signal, said noise suppression system comprising:

means for separating the input signal into a plurality of pre-processed signals representative of selected frequency channels;

means for generating and storing an estimate of the background noise power spectral density of said pre-processed signals, said background noise estimate generating means including means for modifying said background noise estimate in response to a timing parameter indicative of the time interval since the previous background noise estimate modification;

means for generating an estimate of the signal-to-noise ratio (SNR) in each individual channel based upon said modified background noise estimates;

means for producing a gain value for each individual channel in response to said channel SNR estimates; and

means for modifying the gain of each of said plurality of pre-processed signals in response to said gain values to provide a plurality of post-procesed signals.

10. The noise suppression system according to claim 9, wherein said background noise estimate modifying means includes means for producing said timing parameter, and means for comparing said timing parameter to a predetermined timing threshold such that a background noise estimate modification is performed when said timing parameter exceeds said timing threshold.

11. The noise suppression system according to claim 10, wherein said predetermined timing threshold is in the range of 0.5 second to 4 seconds.

12. The noise suppression system according to claim 11, wherein said predetermined timing threshold is approximately equal to 1 second.

13. The noise suppression system according to claim 10, wherein said background noise estimate modifying means further includes means for generating an estimate of the energy in each individual channel, and means for producing a multi-channel energy parameter in response to the total value of all individual channel energy estimates.

14. The noise suppression system according to claim 13, wherein said background noise estimate modifying means further includes means for comparing said multi-channel energy parameter to a predetermined energy threshold such that a background noise estimate modification is performed when said multi-channel energy parameter is less than said energy threshold.

15. The noise suppression system according to claim 13, wherein said multi-channel energy parameter is generated by translating said individual channel SNR estimates into individual channel voice metrics and summing the individual channel voice metrics, the voice metric sum being a measurement of the overall voice-like characteristics of the energy in all channels.

16. The noise suppression system according to claim 14, wherein said background noise estimate modifying means modifies said background noise estimates in response to said timing parameter regardless of said multi-channel energy parameter.

17. The noise suppression system according to claim 13, wherein said multi-channel energy parameter producing means accommodates for minor variations in individual channel energy estimates such that said minor variations do not significantly affect said multi-channel energy parameter.

18. The noise suppression system according to claim 14, wherein said predetermined energy threshold is set such that a background noise estimate modification is performed if all channels exhibit individual SNR values less than 6 dB SNR.

19. The noise suppression system according to claim 14, wherein said predetermined energy threshold is set such that a background noise estimate modification is not performed if any single channel exhibits an SNR value of at least 6 dB SNR.

20. The noise suppression system according to claim 9, wherein said gain value producing means further includes a plurality of gain tables, each gain table having predetermined individual channel gain values corresponding to various individual channel SNR estimates, and gain table selection means for automatically selecting one of said plurality of gain tables as a function of the overall average background noise level of said input signal.

21. The noise suppression system according to claim 9, further includes means for combining said plurality of post-processed signals to produce said noise-suppressed output signal.

22. An improved noise suppression system for attenuating the background noise from a noisy input signal to produce a noise-suppressed output signal, said noise suppression system comprising:

means for separating the input signal into a plurality of pre-processed signals representative of a number N of selected frequency channels

means for generating an estimate of the energy in each individual channel;

means for monitoring said channel energy estimates and for distinguishing narrowband noise bursts from speech energy and background noise energy, thereby producing a modification signal;

means for selectively modifying said channel energy estimates in response to said modification signal such that channel energy estimates representative of narrowband noise bursts are modified;

means for producing a gain value for each individual channel in response to each modified channel energy estimate; and

means for modifying the gain of each of said plurality of pre-processed signals in response to said gain values to provide a plurality of post-processed signals.

23. The noise suppression system according to claim 22, wherein said modification signal is indicative of the total number of individual channels having energy estimates exceeding a predetermined energy threshold.

24. The noise suppression system according to claim 23, wherein said predetermined energy threshold corresponds to a signal-to-noise ratio (SNR) value within the range of 4 dB to 10 dB SNR.

25. The noise suppression system according to claim 24, wherein said predetermined energy threshold corresponds to an SNR value of approximately 6 dB SNR.

26. The noise suppression system according to claim 23, wherein said channel energy estimate modifying means includes means for comparing said modification signal to a predetermined count threshold such that a channel energy estimate modification is performed when said total number of individual channels is less than said count threshold.

27. The noise suppression system according to claim 26, wherein said predetermined count threshold corresponds to less than 40% .times.N.

28. The noise suppression system according to claim 22, wherein said gain modifying means provides a maximum amount of attenuation of the pre-processed signal in a particular channel having a modified channel energy estimate.

29. The noise suppression system according to claim 22, wherein said gain value producing means further includes a plurality of gain tables, each gain table having predetermined individual channel gain values corresponding to various individual channel energy estimates, and gain table selection means for automatically selecting one of said plurality of gain tables as a function of the overall average background noise level of said input signal.

30. The noise suppression system according to claim 22, further includes means for combining said plurality of post-processed signals to produce said noise-suppressed output signal.

31. An improved method of attenuating the background noise from a noisy input signal to produce a noise-suppressed output signal in a noise suppression system comprising the steps of:

separating the input signal into a plurality of preprocessed signals representative of a number N of selected frequency channels;

generating an estimate of the energy in each individual channel;

generating and storing an estimate of the background noise power spectral density of said pre-processed signals;

generating an estimate of the signal-to-noise ratio (SNR) in each individual channel based upon said background noise estimates and said channel energy estimates;

producing a gain value for each individual channel in response to said channel SNR estimates, said gain values having a range of minimal values, said gain value producing step including the steps of providing a predefined SNR threshold and comparing said channel SNR estimates to said predefined SNR threshold such that channels having SNR estimates below said SNR threshold produce gain values within said minimal range; and

modifying the gain of each of said plurality of preprocessed signals in response to said gain values to provide a plurality of post-processed signals.

32. The method according to claim 31, wherein said predefined SNR threshold corresponds to an SNR value within the range of 1.5 dB to 5 dB SNR.

33. The method according to claim 31, wherein said gain modifying step provides a maximum amount of attenuation of the pre-processed signal in a particular channel having a gain value within said minimal range.

34. The method according to claim 31, including the step of modifying said background nose estimate in response to a timing parameter indicative of the time interval since the previous background noise estimate modification.

35. The method according to claim 34, wherein said background noise estimate modifying step includes the steps of producing said timing parameter and comparing said timing parameter to a predetermined timing threshold such that a background noise estimate modification is performed when said timing parameter exceeds said timing threshold.

36. The method according to claim 35, wherein said predetermined timing threshold is in the range of 0.5 second to 4 seconds.

37. The method according to claim 34, wherein said background noise estimate modifying step further includes the step of producing a multi-channel energy parameter in response to the total value of all individual channel SNR estimates.

38. The method according to claim 37, wherein said background noise estimate modifying step further includes the step of comparing said multi-channel energy parameter to a predetermined energy threshold such that a background noise estimate modification is performed when said multi-channel energy parameter is less than said energy threshold.

39. The method according to claim 38, wherein said multi-channel energy parameter is generated by translating said individual channel SNR estimates into individual channel voice metrics and summing the individual channel voice metrics, the voice metric sum being a measurement of the overall voice-like characteristics of the energy in all channels.

40. The method according to claim 38, wherein said background noise estimate modifying step modifies said background noise estimates in response to said timing parameter regardless of said multi-channel energy parameter.

41. The method according to claim 38, wherein said predetermined energy threshold is set such that a background noise estimate modification is performed if all channels exhibit individual SNR values less than 6 dB SNR.

42. The method according to claim 38, wherein said predetermined energy threshold is set such that a background noise estimate modification is not performed if any single channel exhibits an SNR value of at least 6 dB SNR.

43. The method according to claim 31, including the steps of monitoring said channel SNR estimates and distinguishing narrowband noise bursts from speech energy and background noise energy thereby producing a modification signal, and selectively modifying said channel SNR estimates in response to said modification signal such that channel SNR estimates representative of narrowband noise bursts are modified.

44. The method according to claim 43, wherein said modification signal is indicative of the total number of individual channel having SNR estimates exceeding a predetermined modification threshold.

45. The method according to claim 44, wherein said predetermined modification threshold corresponds to an SNR value within the range of 4 dB to 10 dB SNR.

46. The method according to claim 44, wherein said channel SNR estimate modifying step includes the step of comparing said modification signal to a predetermined count threshold such that a channel SNR estimate modification is performed when said total number of individual channels is less than said count threshold.

47. The method according to claim 46, wherein said predetermined count threshold corresponds to less than 40% .times.N.

48. The method according to claim 43, wherein said gain modifying step provides a maximum amount of attenuation of the pre-processed signal in a particular channel having a modified channel SNR estimate.

49. The method according to claim 31, wherein said gain value producing step further includes the step of automatically selecting one of a plurality of gain tables as a function of the overall average background noise level of said input signal, each gain table having predetermined individual channel gain values corresponding to various individual channel SNR estimates.

50. The method according to claim 31, further includes the step of combining said plurality of post-processed signals to produce said noise-suppressed output signal.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application incorporates by reference U.S. Pat. No. 4,628,529, assigned to the same assignee as the present application. Furthermore, this application contains subject matter related to U.S. Pat. No. 4,630,304 and U.S. Pat. No. 4,630,305, also assigned to the same assignee as the present application.

Background of the Invention

1. Field of the Invention

The present invention relates generally to acoustic noise suppression systems. The present invention is more specifically directed to improving the speech quality of a noise suppression system employing the spectral subtraction noise suppression technique.

2. Description of the Prior Art

Acoustic noise suppression in a speech communication system generally serves the purpose of improving the overall quality of the desired audio signal by filtering environmental background noise from the desired speech signal This speech enhancement process is particularly necessary in environments having abnormally high levels of ambient background noise, such as an aircraft, a moving vehicle, or a noisy factory.

The noise suppression technique described in the aforementioned patents is the spectral subtraction--or spectral gain modification--technique Using this approach, the audio input signal is divided into individual spectral bands by a bank of bandpass filters, and particular spectral bands are attenuated according to their noise energy content. A spectral subtraction noise suppression prefilter utilizes an estimate of the background noise power spectral density to generate a signal-to-noise ratio (SNR) of the speech in each channel, which, in turn, is used to compute a gain factor for each individual channel The gain factor is used as a pointer for a look-up table to determine the attenuation for that particular spectral band. The channels are then attenuated and recombined to produce the noise-suppressed output waveform.

In specialized applications involving relatively high background noise environments, most noise suppression techniques exhibit significant performance limitations. One example of such an application is the vehicle speakerphone option to a cellular mobile radio telephone system, which provides hands-free operation for the automobile driver. The mobile hands-free microphone is typically located at a greater distance from the user, such as being mounted overhead on the visor. The more distant microphone delivers a much poorer signal-to-noise ratio to the land-end party due to road and wind noise conditions. Although the received speech at the land-end is usually intelligible, continuous exposure to such background noise levels often increases listener fatigue.

Although most prior art techniques perform sufficiently well under nominal background noise conditions, the performance of known techniques becomes severely limited in such specialized applications of unusually high background noise Typical spectral subtraction noise suppression systems may reduce the background noise level over the voice frequency spectrum by as much as 10 dB without seriously affecting the speech quality. However, when the prior art techniques are used in relatively high background noise environments requiring noise suppression levels approaching 20 dB, there is a substantial degradation in the quaity characteristics of the voice. Furthermore, in rapidly-changing high noise environments, a severe low frequency noise flutter develops in the output speech signal which resembles a distant "jet engine roar" sound. This noise flutter is inherent in a spectral subtraction noise suppression system, since the individual channel gain parameters are continuously being updated in response to the changing background noise environment.

The background noise flutter problem was indirectly addressed but not eliminated through the use of gain smoothing. For example, R.J. McAulay and M.L. Malpass, in the article entitled "Speech Enhancement Using a Soft-Decision Noise Suppression Filter", IEEE Trans. Acoust., Speech, Signal Processing, Vol. ASSP-28, No. 2 (April 1980), pp. 137-145, propose the use of gain smoothing on a per-frame basis to avoid the introduction of discontinuities in the output waveform Since the introduction of gain smoothing can cause the noise suppression prefilter to be slow to respond to a leading edge transition (which would result in speech distortion), a weighting factor of 1 or 1/2 was chosen such that the prefilter responds immediately to an increase in gain while tending to smooth any decrease in gain. Unfortunately, excessive gain smoothing still produces noticeable detrimental effects in voice quality, the primary effect being the apparent introduction of a tail-end echo or "noise pump" to spoken words. There is also a significant reduction in voice amplitude with large amounts of gain smoothing.

The noise flutter performance was further improved by the technique of smoothing the noise suppression gain factors for each individual channel on a per-sample basis instead of on a per-frame basis. Persample smoothing, as well as utilizing different smoothing coefficients for each channel, is described in U.S. Pat. No. 4,630,305, entitled "Automatic Gain Selector for a Noise Suppression System." However, none of the known prior art techniques appreciate that the primary source of the channel gain discontinuities is the inherent fluctuation of background noise in each channel from one frame to the next. In known spectral subtraction systems, even a 2 dB SNR variation would create a few dB of gain variation, which is then heard as an annoying background noise flutter. Hence, the flutter problem has never been effectively solved.

Moreover, narrowband noise--that which has a high power spectral density in only a few channels--further complicates the background noise flutter problem. Since these few high energy noise channels would not be attenuated by the background noise suppressor, the resultant audio output has a "running water" type of characteristic. Narrowband noise bursts also degrade the accuracy of the background noise update decision required to perform noise suppression in changing background noise environments.

Since the gain factors are chosen by SNR estimates, which are determined by the speech energy in each channel (signal) and the current background noise energy estimate in each channel (noise), the performance of the entire noise suppression system is based upon the accuracy of the background noise estimate The statistics of the background noise are estimated during the time when only background noise is present, such as during the pauses in human speech. Therefore, an accurate speech/noise classification must be made to determine when such pauses in speech are occurring.

It is widely known that the energy histogram technique for distinguishing between background noise and speech perform sufficiently well in normal ambient noise environments. See, e.g., W.J. Hess, "A Pitch Synchronous Digital Feature Extraction System for Phonemic Recognition of Speech," IEEE Trans. Acoust., Speech, Signal Processinq, Vol. ASSP-24, No. 1 (February 1976), pp. 14-25. Energy histograms of acoustic signals exhibit a bimodal distribution in which the two modes correspond to noise and speech. Thus, an appropriate threshold can be set between the two modes to provide the speech/noise classification. However, the distinction between background noise energy and unvoiced speech energy in relatively high background noise environments is unclear. Consequently, the task of accurately finding the two modes of the energy histogram, and setting the appropriate threshold between them, is extremely difficult.

To accommodate changing noise backgrounds, McAulay and Malpass implement an adaptive threshold by constantly monitoring the histogram energy on a frame-byframe basis, and updating the threshold utilizing different decay factors. Alternatively, U.S. Pat. No. 4,630,304 utilizes an energy valley detector to perform the speech/noise decision based upon the post-processed signal energy--signal energy available at the output of the noise suppression system--to determine the detected speech minimum Thus, the accuracy of the background noise estimate is improved since it is based upon a much cleaner speech signal.

However, neither prior art technique is properly responsive to a sudden, strong increase in background noise level. These background noise estimate updating decision processes interpret a sudden, loud noise level rise as speech, such that no updates are performed. The energy histogram or valley detector has a slow adaptation characteristic which will eventually adapt to the higher noise level. However, this adaptation characteristic does lead to incorrect noise updates on the weaker energy portions of speech. This erroneous decision significantly degrades the performance of the noise suppression system.

A need, therefore exists for an improved acoustic noise suppression system which addresses the problems of background noise fluctuation, narrowband noise bursts, and sudden background noise increases.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide an improved method and apparatus for suppressing background noise in high background noise environments without significantly degrading the voice quality.

Another object of the present invention is to provide an improved noise suppression system that addresses the background noise fluctuation problem without requiring large amounts of gain smoothing.

A further object of the present invention is to provide a spectral subtraction noise suppression system which compensates for the detrimental effects of narrowband noise bursts.

Another object of the present invention is to provide a background noise estimation mechanism which is not misled by low energy portions of speech, yet still provides correction for sudden, strong increases in background noise levels.

These and other objects are achieved by the present invention which, briefly described, is an improved noise suppression system for attenuating the background noise from a noisy input signal to produce a noise-suppressed output signal by spectral gain modification., The noise suppression system (800) includes a mechanism (210) for separating the input signal into a plurality of pre-processed signals representative of selected frequency channels, a mechanism (310) for generating an estimate of the signal-to-noise ratio (SNR) in each individual channel; a mechanism (590) for producing a gain value for each individual channel by automatically selecting one of a plurality of gain values from a particular gain table in response to the channel SNR estimates, and a mechanism (250) for modifying the gain of each of the plurality of pre-processed signals in response to the selected gain values to provide a plurality of post-processed noisesuppressed output signals. The improvements of the present invention relate to the addition of an SNR threshold mechanism (830) to eliminate minor gain fluctuations for low SNR conditions, a voice metric calculator (810) to produce a more accurate background noise estimate update decision, and a channel SNR modifier (820) to suppress narrowband noise bursts.

More specifically, the first aspect of the present invention pertains to the addition of an SNR threshold mechanism (830) for providing a predetermined SNR threshold which the channel SNR estimates must exceed before a gain value above a predefined minimum gain value can be produced. In the preferred embodiment, the SNR threshold is set at 2.25 dB SNR, such that minor background noise fluctuations do not create step discontinuities in the noise suppression gains.

According to the second aspect of the present invention, a voice metric calculator (810) is utilized to perform the speech/noise classification for the background noise update decision using a two-step process. First, the raw SNR estimates are used to index a vocce metric table to obtain voice metric values for each channel. A voice metric is a measurement of the overall voice-like characteristics of the channel energy. The individual channel voice metric values are summed to create a first multi-channel energy parameter, and then compared to a background noise update threshold. If the voice metric sum does not meet the threshold, the input frame is deemed to be noise, and a background noise update is performed. Secondly, the time since the occurrence of the previous background estimate update is constantly monitored. If too much time has passed since the last update, e.g., 1 second, then it is assumed that a substantial increase in noise has occurred, and a background noise update is performed regardless of whether it looks like a voice frame. This second test is based on the assumption that speech seldom contains continuous high energy levels in all channels for more than one second, which would be the case for a sudden, loud noise level increase. The voice metric algorithm incorporating the two-step decision process provides a very accurate background noise estimate update signal.

In the third aspect of the present invention, a channel SNR modifying mechanism (820) provides a second multi-channel energy parameter in response to the number of upper-channel SNR estimates which exceed a predetermined energy threshold, e.g., 6 dB SNR. If only a few channels have an energy level above this energy threshold (such as would be the case for a narrowband noise burst), the measured SNR for those particular channels would be reduced. Moreover, if the aforementioned voice metric sum is less than a metric threshold (which would indicate that the frame was noise), all channels are similarly reduced. This SNR modifying technique is based on the assumption that typical speech exhibits a majority of channels having signal-to-noise ratios of 6 dB or greater.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the present invention which are believed to be novel are set forth with particularity in the appended claims. The invention itself, however, together with further objects and advantages thereof, may best be understood by reference to the following description when in conjunction with the accompanying in which:

FIG. 1 is a detailed block diagram illustrating the preferred embodiment of the improved noise suppression system according to the present invention;

FIG. 2 is a graph representing voice metric values output as a function of SNR estimate index values input for the voice metric calculator block of FIG. 1;

FIG. 3 is a representative gain table graph illustrating the overall channel attenuation for particular groups as a function of the SNR estiaate; and

FIGS. 4a through 4f are flowcharts illustrating the specific sequence of operations performed in accordance with the practice of the preferred embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 is a detailed block diagram of the preferred embodiment of the present invention. All the elements of FIG. 1 having reference numerals less than 600 correspond to those of U.S. Pat. No. 4,628,529-Borth et al., which is incorporated herein by reference. Refer to the Borth patent for their description. The additional circuit components having reference numerals greater than 600 represent the improvements to the system, and will be described herein.

Improved noise suppression 800 incorporates changes to the aforementioned Borth noise suppression system in three basic areas: (a) the updating of background noise estimates by voice metric calculator 810; (b) the modification of SNR estimates by channel SNR modifier 820; and (c) utilization of SNR thresh