 |
Claims  |
|
|
What is claimed is:
1. An improved background noise estimator adapted for use with a noise
suppression system wherein the background noise from a noisy pre-processed
input signal is attenuated by spectral gain modification to produce a
noise-suppressed post-processed output signal, said background noise
estimator comprising:
noise estimation means for generating and storing an estimate of the
background noise power spectral density of the pre-processed signal; and
noise detection means for periodically detecting the minima of the
post-processed signal energy, and for controlling said noise estimation
means in response thereto such that said background noise estimate is
updated only during said minima.
2. The background noise estimator according to claim 1, wherein said noise
estimation means includes:
channel energy estimation means for generating an estimate of the
pre-processed signal energy in each of a plurality of selected frequency
bands; and
storage means for storing each of said energy estimates as a per-channel
noise estimate, and for continuously providing an estimate of the
background noise power spectral density of the pre-processed signal to
said noise suppression system.
3. The background noise estimator according to claim 2, wherein said
channel energy estimation means includes:
means for separating said pre-processed signal into a plurality of
frequency channels; and
means for detecting the energy in each of said channels.
4. The background noise estimator according to claim 3, wherein said
separating means includes a plurality of bandpass filters covering the
voice frequency range.
5. The background noise estimator according to claim 4, wherein said
plurality of bandpass filters is further comprised of a bank of
approximately 14 contiguous bandpass filters covering the frequency range
from approximately 250 Hz. to 3400 Hz.
6. The background noise estimator according to claim 3, wherein said
detecting means includes a plurality of full-wave rectifiers coupled to
low-pass filters, thereby providing an energy estimate for each channel.
7. The background noise estimator according to claim 2, wherein said
storage means includes:
smoothing means for providing a time-averaged value of each of said energy
estimates generated by said channel energy estimation means; and
memory means for storing each of said time-averaged values from said
smoothing means as per-channel noise estimates.
8. The background noise estimator according to claim 7, wherein said memory
means is preset upon system initialization with initialization values
which represent per-channel noise estimates approximating that of a clean
input signal.
9. The background noise estimator according to claim 1, wherein said noise
detection means includes:
channel energy estimation means for generating an estimate of the
post-processed signal energy in each of a plurality of selected frequency
bands;
channel combination means for combining the plurality of said energy
estimates into a single overall energy estimate;
valley detection means for periodically detecting the minima of said
overall energy estimate, thereby generating a valley detect signal; and
signal controlling means coupled to said noise estimation means and
controlled by said valley detect signal for providing new background noise
estimates to said noise estimation means only during said minima.
10. The background noise estimator according to claim 9, wherein said
channel energy estimation means includes:
means for separating said post-processed signal into a plurality of
frequency channels; and
means for detecting the energy in each of said channels.
11. The background noise estimator according to claim 10, wherein said
separating means includes a plurality of bandpass filters covering the
voice frequency range.
12. The background noise estimator according to claim 11, wherein said
plurality of bandpass filters is further comprised of a bank of
approximately 14 contiguous bandpass filters covering the frequency range
from approximately 250 Hz. to 3400 Hz.
13. The background noise estimator according to claim 10, wherein said
detecting means includes a plurality of full-wave rectifiers coupled to
low-pass filters, thereby providing an energy estimate for each channel.
14. The background noise estimator according to claim 9, wherein said
channel combination means includes means for summing the plurality of
detected energy estimates to provide a single overall energy estimate.
15. The background noise estimator according to claim 9, wherein said
valley detection means includes:
means for storing the numerical value of the previous detected minima as a
previous valley level;
means for comparing the present numerical value of the overall energy
estimate to said previous valley level;
means for increasing said previous valley level at a slow rate when said
present numerical value is greater than said previous valley level; and
means for decreasing said previous valley level at a rapid rate when said
present numerical value is less than said previous valley level, thereby
updating said previous valley level to provide a current valley level.
16. The background noise estimator according to claim 15, wherein said
rapid rate for updating said previous valley level exhibits a time
constant of approximately 40 milliseconds.
17. The background noise estimator according to claim 15, wherein said slow
rate for updating said previous valley level exhibits a time constant of
approximately 1000 milliseconds.
18. The background noise estimator according to claim 15, wherein said
valley detection means further includes:
means for adding a selected valley offset to said current valley level,
thereby providing a noise threshold level; and
means for comparing said present numerical value to said noise threshold
level, thereby generating a positive valley detect signal only when said
present numerical value is less than said noise threshold level.
19. The background noise estimator according to claim 18, wherein said
selected valley offset is approximately 6 dB relative to said current
valley level.
20. The background noise estimator according to claim 18, wherein said
present numerical value and said previous valley level are expressed in
logarithmic terms.
21. The background noise estimator according to claim 9, wherein said
signal controlling means includes:
channel switch means coupled to said noise estimation means and controlled
by said valley detect signal for providing new background noise estimates
to said noise estimation means only when said valley detect signal is
positive.
22. An improved background noise estimator adapted for use with a noise
suppression system wherein the background noise from a noisy pre-processed
input signal is attenuated by spectral gain modification to produce a
noise-suppressed post-processed output signal, said background noise
estimator comprising:
storage means for storing an estimate of the background noise energy of the
pre-processed signal in each of a plurality of selected frequency bands as
per-channel noise estimates, and for continuously providing an estimate of
the background noise power spectral density of the pre-processed signal to
said noise suppression system;
valley detection means for periodically detecting the minima of an overall
estimate of the energy of said post-processed signal in each of a
plurality of selected frequency bands, thereby generating a valley detect
signal; and
signal controlling means coupled to said storage means and controlled by
said valley detect signal for providing new background noise estimates to
said storage means only during said minima.
23. The background noise estimator according to claim 22, wherein said
storage means includes:
smoothing means for providing a time-averaged value of each of said
background noise energy estimates of the pre-processed signal in a
particular frequency band; and
memory means for storing each of said time-averaged values from said
smoothing means as per-channel noise estimates.
24. The background noise estimator according to claim 23, wherein said
memory means is preset upon system initialization with initialization
values which represent per-channel noise estimates approximating that of a
clean input signal.
25. The background noise estimator according to claim 22, wherein said
valley detection means includes:
means for storing the numerical value of the previous detected minima as a
previous valley level;
means for comparing the present numerical value of the overall energy
estimate to said previous valley level;
means for increasing said previous valley level at a slow rate when said
present numerical value is greater than said previous valley level; and
means for decreasing said previous valley level at a rapid rate when said
present numerical value is less than said previous valley level, thereby
updating said previous valley level to provide a current valley level.
26. The background noise estimator according to claim 25, wherein said
rapid rate for updating said previous valley level exhibits a time
constant of approximately 40 milliseconds.
27. The background noise estimator according to claim 25, wherein said slow
rate for updating said previous valley level exhibits a time constant of
approximately 1000 milliseconds.
28. The background noise estimator according to claim 25, wherein said
valley detection means further includes:
means for adding a selected valley offset to said current valley level,
thereby providing a noise threshold level; and
means for comparing said present numerical value to said noise threshold
level, thereby generating a positive valley detect signal only when said
present numerical value is less than said noise threshold level.
29. The background noise estimator according to claim 28, wherein said
selected valley offset is approximately 6 dB relative to said current
valley level.
30. The background noise estimator according to claim 22, wherein said
signal controlling means includes:
channel switch means coupled to said storage means and controlled by said
valley detect signal for providing new background noise estimates to said
storage means only when said valley detect signal is positive.
31. The background noise estimator according to claim 28, wherein said
present numerical value and said previous valley level are expressed in
logarithmic terms.
32. The method of estimating background noise in a noise suppression
system, wherein the background noise from a noisy pre-processed input
signal is attenuated by spectral gain modification to produce a
noise-suppressed post-processed output signal, comprising the steps of:
periodically detecting the minima of the post-processed signal energy;
providing a noise detection signal only when said minima is detected; and
generating and storing an estimate of the background noise power spectral
density of the pre-processed signal only during the presence of said noise
detection signal.
33. The method of estimating background noise in a noise suppression
system, wherein the background noise from a noisy pre-processed input
signal is attenuated by spectral gain modification to produce a
noise-suppressed post-processed output signal, comprising the steps of:
periodically detecting the minima of an overall estimate of the energy of
the post-processed signal in each of a plurality of selected frequency
bands;
providing a positive valley detect signal only when said minima is
detected; and
storing an estimate of the energy of the pre-processed signal in each of a
plurality of selected frequency bands only during the presence of said
positive valley detect signal. |
|
 |
|
 |
Claims |
|
|
|
Description |
|
|
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to noise suppression systems, and,
more particularly, to a novel technique for estimating the background
noise power spectrum for a spectral subtraction noise suppression system.
2. Description of the Prior Art
Acoustic noise suppression has been implemented in a wide variety of speech
communications, varying from basic hearing aid applications to highly
sophisticated military aircraft communications systems. The common
objective in all such noise suppression systems is that of enhancing the
quality of speech in an environment having a relatively high level of
ambient background noise. The acoustic noise suppression system must
augment the quality characteristics of the speech signal by reducing the
background noise level without significantly degrading the voice
intelligibility.
A possible solution to this problem is to incorporate an acoustic noise
suppression prefilter, which effectively subtracts an estimate of the
background noise signal from the noisy speech waveform, to perform the
noise cancellation function. One technique for obtaining the estimate of
the background noise is to implement a second microphone, located at a
distance away from the user's first microphone, such that it picks up only
background noise. This technique has been shown to provide a significant
improvement in signal-to-noise ratio (SNR). However, it is very difficult
to achieve the required isolation of the second microphone from the speech
source while at the same time attempting to pick up the same background
noise environment as the first microphone.
Another method for obtaining the background noise estimate is to estimate
statistics of the background noise during the time when only background
noise is present, such as during the pauses in human speech. This method
is based on the assumption that the background noise is predominantly
stationary, which is a valid assumption for many types of noise
environments. Therefore, some mechanism for discriminating between
background noise and speech is required.
Several approaches to the problem of distinguishing between speech and
noise are known in the art. A summary of some of these techniques is found
in P. De Souza, "A Statistical Approach to the Design of an Adaptive
Self-Normalizing Silence Detector," IEEE Trans. Acoust., Speech, Signal
Processing, vol. ASSP-31, no. 3, (June 1983), pp. 678-684, and the
references contained therein. These prior art techniques implement various
combinations of: (a) frame-to-frame energy; (b) zero-crossing rate; and
(c) autocorrelation function or LPC coefficients.
In abnormally high noise environments, such as a moving vehicle, many of
these known and referenced prior art techniques break down. For example,
it has been widely documented that many types of noise do not lend
themselves to an all-pole model, thereby not permitting an LPC fit.
Furthermore, discrimination between speech and noise in a high background
noise environment on the basis of zero-crossings has also been shown to be
ineffective due to the similar zero crossing characteristics of speech and
noise.
The frame energy parameter has been found to be the most effective
technique to discriminate between noise and speech. Consequently, the
majority of speech recognition systems and communications systems which
are designed for use in high ambient noise environments makes use of some
variation of this technique.
Unfortunately, the speech/noise classification on the basis of frame energy
measurements has been effective only for voiced sounds due to the similar
energy characteristics of unvoiced sounds and background noise. It is
widely known that the energy histogram technique for distinguishing
between speech and noise performs sufficiently well in normal ambient
noise environments. Since energy histograms of acoustic signals exhibit a
bimodal distribution, in which the two modes correspond to noise and
speech, then an appropriate threshold can be set between the two modes to
provide the speech/noise classification. (See, e.g., W. J. Hess, "A
Pitch-Synchronous Digital Feature Extraction System for Phonemic
Recognition of Speech," IEEE Trans. Acoust., Speech, Signal Processing,
vol. ASSP-24, no. 1 (February 1976), pp. 14-25.) The disadvantage of this
approach is that the distinction between background noise energy and
unvoiced speech energy in relatively high 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.
SUMMARY OF THE INVENTION
It is, therefore, a primary object of the present invention to provide an
improved method and apparatus for estimating the background noise power
spectrum for use with an acoustic noise suppression system.
A more particular object of the present invention is to provide a method
and apparatus to determine when the input signal contains only background
noise as distinguished from an input signal containing speech plus
background noise.
Still another object of the present invention is to provide a means for
automatically updating the previous background noise estimate during those
periods when only background noise is present.
In practicing the invention, an apparatus and method is provided for
automatically performing background noise estimation for use with an
acoustic noise suppression system, wherein the background noise from a
noisy pre-processed input signal--the speech-plus-noise signal available
at the input of the noise suppression system--is attenuated to produce a
noise-suppressed post-processed output signal--speech-minus-noise signal
provided at the output of the noise suppression system--by spectral gain
modification. The automatic background noise estimator includes a noise
estimation means which generates and stores an estimate of the background
noise power spectral density based upon the pre-processed input signal.
The background noise estimator of the present invention further includes a
noise detection means, such as an energy valley detector, which performs
the speech/noise decision based upon the post-processed signal energy
level. The noise detection means provides this speech/noise decision to
the noise estimation means such that the background noise estimate is
updated only when the detected minima of the post-processed signal energy
is below a predetermined threshold. The novel technique of implementing
post-processed speech energy for the noise detection means, thereby
controlling the pre-processed speech energy to the noise estimation means,
allows the present invention to generate a highly accurate background
noise estimate for an acoustic noise suppression system.
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 taken in
conjunction with the accompanying drawings, in which:
FIG. 1 is a block diagram of a basic noise suppression system known in the
art which illustrates the spectral gain modification technique;
FIG. 2 is a block diagram of an alternate implementation of a prior art
noise suppression system illustrating the channel filter-bank technique;
FIG. 3 is a simplified block diagram of an improved acoustic noise
suppression system employing the automatic background noise estimator of
the present invention;
FIG. 4 is a detailed block diagram of the automatic background noise
estimator of FIG. 3;
FIG. 5 is a flowchart illustrating the general sequence of operations
performed in accordance with the practice of the present invention; and
FIG. 6 is a detailed flowchart illustrating the specific sequence of
operations shown in FIG. 5.
DESCRIPTION OF THE PREFERRED EMBODIMENT
Referring now to the drawings, FIG. 1 is a block diagram of basic noise
suppression system 100 implementing spectral gain modification as is well
known in the art. A continuous time signal containing speech-plus-noise is
applied to input 102 of the noise suppressor where it is then converted to
digital form by analog-to-digital converter 105. This digital data is then
segmented into blocks of data by the windowing operation (e.g., Hamming,
Hanning, or Kaiser windowing techniques) performed by window 110. The
choice of the window is similar to the choice of the filter response in an
analog spectrum analysis. The noisy speech signal is converted into the
frequency domain by Fast Fourier Transform (FFT) 115. The power spectrum
of the noisy speech signal is then calculated by magnitude squaring
operation 120, and applied to background noise estimator 125 and to power
spectrum modifier 130.
The background noise estimator performs two basic functions: (1) it
determines when the incoming speech-plus-noise signal contains only
background noise; and (2) it updates the old background noise power
spectral density estimate when only background noise is present. The
current estimate of the background noise power spectrum is removed from
the speech-plus-noise power spectrum by power spectrum modifier 130, which
ideally leaves only the power spectrum of clean speech. The square root of
the clean speech power spectrum is then calculated by magnitude square
root operation 135. This magnitude of the clean speech signal is combined
with phase information 145 of the original signal, and converted from the
frequency domain back into the time domain by Inverse Fast Fourier
Transform (IFFT) 140. The discrete data segments of the clean speech
signal are then applied to overlap-and-add operation 150 to reconstruct
the processed signal. This digital signal is then re-converted by
digital-to-analog converter 155 to an analog waveform available at output
158. Thus, an acoustic noise suppressor employing the spectral gain
modification technique requires an accurate estimate of the current
background noise power spectral density to perform the noise cancellation
function.
A drawback of the Fourier Transform approach of FIG. 1 is that it is a
digital signal processing method requiring considerable computational
power to implement the noise suppression prefilter in the frequency
domain. An alternate implementation of the noise suppression prefilter is
the channel filter-bank technique illustrated in FIG. 2. In this approach,
the input signal power spectral density is computed on a per-channel basis
by using contiguous narrowband bandpass filters followed by full-wave
rectifiers and low-pass filters. The background noise is then subtracted
from the noisy speech signal by reducing the gains of the individual
channel bandpass filters before recombination. This time domain
implementation is preferable for use in speech recognition systems and
noise suppression systems, since it is much more computationally efficient
than the FFT approach.
FIG. 2 illustrates channel filter-bank noise suppression prefilter 200. The
speech-plus-noise signal is applied to pre-emphasis network 205 via input
202. The input signal is pre-emphasized to increase the gain of the high
frequency noise and unvoiced components (at +6 dB per octave), since these
components are normally lower in energy as compared to low frequency
voiced components. The pre-emphasized signal is then fed to filter-bank
210, which consists of a number N of contiguous bandpass filters. The
filters overlap at the 3 dB points such that the reconstructed output
signal exhibits less than 1 dB of ripple in the entire voice frequency
range. In the present embodiment, 14 Butterworth bandpass filters are used
to span the voice frequency band of 250-3400 Hz. The 14 channel filter
outputs are then rectified by full-wave rectifiers 215, and smoothed by
low-pass filters 220 to obtain an energy envelope value E.sub.l -E.sub.N
for each channel. These channel energy estimates are applied to channel
noise estimator 225 which provides an SNR estimate X.sub.l -X.sub.N for
each channel. These SNR estimates are then fed to channel gain controller
230 which produces individual channel gains G.sub.l -G.sub.N.
The value of the channel gains is dependent upon the SNR of the detected
signal. When voice is present in an individual channel, the channel
signal-to-noise ratio estimate will be high. Thus, channel gain controller
230 will increase the gain for that particular channel. The amount of the
gain rise is dependent on the detected SNR--the greater the SNR, the more
the individual channel gain will be raised from the base gain (all noise).
If only noise is present in the individual channel, the SNR estimate will
be low, and the gain for that channel will be reduced to the base gain.
Since voice energy does not appear in all of the channels at the same
time, the channels containing a low voice energy level (mostly background
noise) will be suppressed (subtracted) from the voice energy spectrum.
The amplitudes of the individual channel signals output from bandpass
filters 210 are multiplied by the corresponding channel gains G.sub.l
-G.sub.N at channel multipliers 235. The channels are then recombined at
summation circuit 240, and de-emphasized (at -6 dB per octave) by
de-emphasis network 245 to provide clean speech at output 248. Hence, the
channel filter-bank technique simply suppresses the background noise in
the individual channels which have a low signal-to-noise ratio.
Channel noise estimator 225 typically generates SNR estimates X.sub.l
-X.sub.N by comparing the total amount of signal-plus-noise energy in a
particular bandpass filter to some type of estimate of the background
noise. This background noise estimate may be generated by performing a
channel energy measurement during the pauses in human speech. Thus, the
problem then becomes one of accurately locating the pauses in speech such
that the background noise energy can be measured during that precise time
interval. The present invention is specifically addressed to the solution
of this problem.
As previously mentioned, numerous techniques for distinguishing between
speech and noise are known in the art. For example, the energy histogram
technique monitors the energy on a frame-by-frame basis to maintain an
energy histogram which reflects the bimodal distribution of the energy. An
energy threshold mark is generated to provide the probable boundary line
between noise and speech-plus-noise. This threshold may be updated with a
current threshold candidate when the background noise energy changes. A
more detailed description of the energy histogram technique can be found
in R. J. McAulay and M. L. Malpass, "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.
Another approach for detecting pauses in human speech is the valley
detector technique. A valley detector follows the minima of the
envelope-detected speech signal energy by falling rapidly as the signal
level decreases (speech not present), but rising slowly when the signal
level increases (speech present). Thus, the valley detector maintains a
history (previous valley level) essentially corresponding to the steady
state background noise present at the input. When an instantaneous value
of the envelope-detected speech signal energy is compared against this
previous valley level, the comparator is able to distinguish between
speech signals and background noise.
Both methods for making the speech/noise decision, the energy histogram
technique and the valley detector technique, have heretofore been
implemented by utilizing pre-processed speech--the speech-plus-noise
energy available at the input of the noise suppression system. This
practice of using pre-processed speech places inherent limitations upon
the effectiveness of either technique to make an accurate speech/noise
classification. As previously noted, this limitation is due to that fact
that the energy characteristics of unvoiced speech sounds are very similar
to the energy characteristics of background noise. Thus, the accuracy of
the speech/noise decision is directly related to the SNR characteristics
of the input signal energy. One of the most significant aspects of the
present invention involves this recognition that the inaccuracy of the
speech/noise decisions represents a substantial impediment to advancements
in background noise elimination.
If, however, the speech/noise decision where based upon post-processed
speech--the speech energy available at the output of the noise suppression
system--then the accuracy of the speech/noise decision process would be
greatly enhanced by the noise suppression system itself. In other words,
by utilizing the post-processed speech signal, the background noise
estimator operates on a much cleaner speech signal such that a more
accurate speech/noise classification can be performed. The present
invention teaches this unique concept of implementing post-processed
speech signal to base these speech/noise decisions upon. Accordingly, more
accurate determinations of the pauses in speech are made, and better
performance of the noise suppressor is achieved.
This novel technique of the present invention is illustrated in FIG. 3,
which shows a simplified block diagram of improved acoustic noise
suppression system 300. Noise suppressor 310 performs speech quality
enhancement upon the pre-processed speech-plus-noise signal available at
the input, and generates clean post-processed speech at the output. Noise
suppressor 310 utilizes the background noise estimate generated by
background noise estimator 320 to perform the spectral subtraction
process. Background noise estimator 320 uses post-processed speech in
performing the speech/noise classification to determine when the input
signal contains only background noise. It is during this time that the
background noise estimator measures the energy of the pre-processed speech
signal to generate the actual background noise estimate. As a result, the
background noise estimate supplied to the noise suppressor is a more
accurate measurement of the background noise energy, since it is performed
during a more accurate determination of the occurrences of the pauses in
speech.
FIG. 4 shows a more detailed block diagram of background noise estimator
320 of FIG. 3. In generating the background noise estimate to the noise
suppressor, two basic functions must be performed. First, a determination
must be made as to when the incoming speech-plus-noise signal contains
only background noise--during the pauses in human speech. Secondly, this
determination is utilized to control the time at which the background
noise measurement is taken, thereby providing a mechanism to update the
old background noise estimate.
The first function, that of performing the speech/noise classification in a
varying background noise environment, is accomplished by using the valley
detector technique on speech signal obtained from the output of the noise
suppression system. This post-processed speech signal is input to channel
energy estimator 450 which forms individual per-channel energy estimates.
Channel energy estimator 450 is comprised of an N-band
contiguous-frequency filter-bank, and a set of N energy detectors at the
output of each bandpass filter. Each energy detector may consist of a
full-wave rectifier, followed by a second-order Butterworth low-pass
filter, possibly followed by another full-wave rectifier. In the preferred
embodiment, the entire background noise estimator 320 is digitally
implemented, and this implementation will subsequently be described in
FIGS. 5 and 6. Furthermore, channel energy estimator 450 may be one of
several distinct filter/energy detector networks (or equivalent software
code blocks) as illustrated in FIG. 4, or may alternately be combined with
similar estimators elsewhere in the noise suppression system (or performed
as a software subroutine).
In either case, these individual channel energy estimates are fed to
channel energy combiner 460 which provides a single overall energy
estimate for energy valley detector 440. Channel energy combiner 460 may
be omitted if multiple valley detectors are utilized on a per-channel
basis and the valley detector output signals are combined.
Energy valley detector 440 utilizes the overall energy estimate from
combiner 460 to detect the pauses in speech. This is accomplished in three
steps. First, an initial valley level is established. If the background
noise estimator has not previously been initialized, then an initial
valley level is created by loading initialization value 455. Otherwise,
the previous valley level is maintained as its post-processed background
noise energy history.
Next, the previous (or initialized) valley level is updated to reflect
current background noise conditions. This is accomplished by comparing the
previous valley level to the value of the single overall energy estimate
from combiner 460. A current valley level is created by this updating
process, which will be described in detail in FIG. 6b.
The third step performed by energy valley detector 440 is that of making
the actual speech/noise decision. A preselected valley level offset,
represented in FIG. 4 by valley offset 445, is added to the updated
current valley level to produce a noise threshold level. Then the value of
the single overall (post-processed) energy estimate is again compared,
only this time to the noise threshold level. When this energy estimate is
less than the noise threshold level, energy valley detector 440 generates
a speech/noise control signal (valley detect signal) indicating that no
voice is present.
The second basic function of the background noise estimator is accomplished
by applying this valley detect signal to channel switch 410 to cause | | |
