Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission
Reexamination Certificate
2000-06-01
2001-11-13
Dorvil, Richemond (Department: 2641)
Data processing: speech signal processing, linguistics, language
Speech signal processing
For storage or transmission
C704S226000, C381S094300
Reexamination Certificate
active
06317709
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to methods of noise suppression using acoustic spectral subtraction.
BACKGROUND OF THE INVENTION
Acoustic noise suppression in a speech communication system generally serves the purpose of improving the overall quality of the desired audio or speech signal by filtering environmental background noise from the desired speech signal. This speech enhancement process is particularly necessary in environments having abnormally high level of background noise.
Reference is now made to
FIG. 1
which illustrates one noise suppressor which uses spectral subtraction (or spectral gain modification). The noise suppressor includes frequency and time domain converters
10
and
12
, respectively, and a noise attenuator
14
.
The frequency domain converter
10
includes a bank of bandpass filters which divide the audio input signal into individual spectral bands. The noise attenuator
14
attenuates particular spectral bands according to their noise energy content. To do so, the attenuator
14
includes an estimator
16
and a channel gain determiner
18
. Estimator
16
estimates the background noise and signal power spectral densities (PSDs) to generate a signal to noise ratio (SNR) of the speech in each channel. The channel gain determiner
18
uses the SNR to compute a gain factor for each individual channel and to attenuate each spectral band. The attenuation is performed by multiplying, via a multiplier
20
, the signal of each channel by its gain factor. The channels are recombined and converted back to the time domain by converter
12
, thereby producing a noise suppressed signal.
For example, in the article by M. Berouti, R. Schwartz, and J. Makhoul, “Enhancement of Speech Corrupted by Acoustic Noise”,
Proceedings of the IEEE International Conference on Acoustic Speech Signal Processing,
pp. 208-211, April 1979, which is incorporated herein by reference, the method of linear spectral subtraction is discussed. In this method, the channel gain &ggr;
ch
(i) is determined by subtracting the noise power spectrum from the noisy signal power spectrum. In addition, a spectral floor &bgr; is used to prevent the gain from descending below a lower bound, &bgr;|&Egr;
n
(i)|.
The gain is determined as follows:
γ
ch
(
i
)
=
&LeftBracketingBar;
D
(
i
)
&RightBracketingBar;
&LeftBracketingBar;
E
ch
(
i
)
&RightBracketingBar;
where:
D
(
i
)
=
{
&LeftBracketingBar;
E
ch
(
i
)
&RightBracketingBar;
-
&LeftBracketingBar;
E
n
(
i
)
&RightBracketingBar;
if
&LeftBracketingBar;
E
ch
(
i
)
&RightBracketingBar;
-
&LeftBracketingBar;
E
n
(
i
)
&RightBracketingBar;
≥
β
&LeftBracketingBar;
E
n
(
i
)
&RightBracketingBar;
β
&LeftBracketingBar;
E
ch
(
i
)
&RightBracketingBar;
|&Egr;
ch
(i)| is the smoothed estimate of the magnitude of the corrupted speech in the ith channel and |&Egr;
n
(i)| is the smoothed estimate of the magnitude of the noise in the ith channel.
FIG. 2
illustrates the channel gain function &ggr;
ch
(i) per channel SNR ratio and indicates that the channel gain has a short floor
21
after which the channel gain increases monotonically.
Unfortunately, the noise suppression can cause residual ‘musical’ noise produced when isolated spectral peaks exceed the noise estimate for a very low SNR input signal.
FIGS. 3A and 3B
, to which reference is now made, illustrate the typical channel energy in an input signal and the linear spectral subtraction, gain signal, over time. The energy signal of
FIG. 3A
shows high energy speech peaks
22
between which are sections of noise
23
. The gain function of
FIG. 3B
has accentuated areas
24
, corresponding to the peaks
22
, and significant fluctuations
25
between them, corresponding to the sections of noise in the original energy signal. The gains in the accentuated areas
24
cause the high energy speech of the peaks
22
to be heard clearly. However, the gain in the fluctuations
25
, which are of the same general strength as the gain in the accentuated areas
24
, cause the musical noise to be heard as well.
The following articles and patents discuss other noise suppression algorithms and systems:
G. Whipple, “Low Residual Noise Speech Enhancement Utilizing Time-Frequency Filtering”,
Proceedings of the IEEE International Conference on Acoustic Speech Signal Processing,
Vol. I, pp. 5-8, 1994; and
U.S. Pat. Nos. 5,012,519 and 5,706,395.
SUMMARY OF THE INVENTION
An object of the present invention is to provide a method for suppressing the musical noise. This method is based on linear, spectral subtraction but incorporates a weighted gain smoothing mechanism to suppress the musical noise while minimally affecting speech.
There is therefore provided, in accordance with a preferred embodiment of the present invention, a noise suppressor which includes a signal to noise ration (SNR) determiner, a channel gain determiner, a gain smoother and a multiplier. The SNR determiner determines the SNR per channel of the input signal. The channel gain determiner determines a channel gain &ggr;
ch
(i) per the ith channel. The gain smoother produces a smoothed gain {overscore (&ggr;
ch
+L (i,m))} per the ith channel and the multiplier multiplies each channel of the input signal by its associated smoothed gain {overscore (&ggr;
ch
+L (i,m))}.
Additionally, in accordance with a preferred embodiment of the present invention, the smoothed gain {overscore (&ggr;
ch
+L (i,m))} is a function of a previous gain value {overscore (&ggr;
ch
+L (i,m−1+L ))} for the ith channel and a forgetting factor &agr; which is a function of the current level of the SNR for the ith channel.
Additionally, in accordance with a preferred embodiment of the present invention, the forgetting factor &agr; ranges between MAX_ALFA and MIN_ALFA according to the function
1
-
σ
(
i
,
m
)
SNR_DR
where &sgr;(i,m) is the SNR of the current frame m of the ith channel and SNR_DR is the allowed dynamic range of the SNR. For example, MAX_ALFA=1.0, MIN_ALFA=0.01 and SNR_DR=30 dB.
Furthermore, in accordance with a preferred embodiment of the present invention, the forgetting factor &agr; is determined by:
α
=
min
{
MAX_ALFA
,
max
{
MIN_ALFA
,
1
-
σ
(
i
,
m
)
SNR_DR
}
}
Additionally, in accordance with a preferred embodiment of the present invention, the smoothed gain {overscore (&ggr;
ch
+L (i,m))} is set to be either the channel gain &ggr;
ch
(i) or a new value, wherein the new value is provided only if the channel gain &ggr;
ch
(i)for the current frame m is greater than the smoothed gain {overscore (&ggr;
ch
+L (i,m−1+L ))} for the previous frame m−1.
Additionally, in accordance with a preferred embodiment of the present invention, the smoothed gain {overscore (&ggr;
ch
+L (i,m))} is defined by:
γ
ch
(
i
,
m
)
_
=
{
α
·
γ
ch
(
i
,
m
-
1
)
_
+
(
1
-
α
)
·
γ
ch
(
i
,
m
)
if
γ
ch
(
i
,
m
)
≥
γ
ch
(
i
,
m
-
1
)
_
γ
ch
(
i
,
m
)
Otherwise
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patent: 4630305 (1986-12-01), Borth et al.
patent: 4811404 (1989-03-01), Vilmur et al.
patent: 5012519 (1991-04-01), Adlesberg et al.
patent: 5432859 (1995-07-01), Yang et al.
patent: 5544250 (1996-08-01), Urbanski
patent: 5550924 (1996-08-01), Helf et al.
patent: 5659622 (1997-08-01), Ashley
patent: 5666429 (1997-09-01), Urbanski
patent: 5706395 (1998-01-01), Arslan et al.
patent: 5844951 (1998-12-01), Proakis et al.
patent: 5937377 (1999-10-01), Hardiman et al.
patent: 6088668 (2000-07-01), Zack
Tim Haulick, “Residual Noise Suppression Using Psychoacoustic Criteria”, ESCA Eurospeech 97, Rhodes, Greece, ISSN 1018-4074, pp. 1395-1398.
Pascal Scalart et al., “Speech Enhancement Based on a Prior Signal To Noise Estimation”, 0-7803-3192-3/96, IEEE 1996, pp. 629-632.
Gary W
D.S.P.C. Technologies Ltd.
Dorvil Richemond
Eitan Pearl Latzer & Cohen-Zedek
Lerner Martin
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