Note: Descriptions are shown in the official language in which they were submitted.
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TITLE OF INVENTION
PERCEPTUAL SPECTRAL WEIGHTING OF FREQUENCY BANDS FOR
ADAPTIVE NOISE CANCELLATION
BACKGROUND OF THE INVENTION
This invention relates to communication system noise cancellation techniques,
and more particularly relates to weighting calculations used in such
techniques.
The need for speech quality enhancement in single-channel speech
communication systems has increased in importance especially due to the
tremendous
growth in cellular telephony. Cellular telephones are operated often in the
presence of
high levels of environmental background noise, such as in moving vehicles.
Such
high levels of noise cause significant degradation of the speech quality at
the far end
receiver. In such circumstances, speech enhancement techniques may be employed
to
improve the quality of the received speech so as to increase customer
satisfaction and
encourage longer talk times.
Most noise suppression systems utilize some variation of spectral subtraction.
Figure 1A shows an example of a typical prior noise suppression system that
uses
spectral subtraction. A spectral decomposition of the input noisy speech-
containing
signal is first performed using the Filter Bank. The Filter Bank may be a bank
of
bandpass filters (such as in reference [1], which is identified at the end of
the
description of the preferred embodiments). The Filter Bank decomposes the
signal
into separate frequency bands. For each band, power measurements are performed
and continuously updated over time in the Noisy Signal Power & Noise Power
Estimation block. These power measures are used to determine the signal-to-
noise
ratio (SNR) in each band. The Voice Activity Detector is used to distinguish
periods
of speech activity from periods of silence. The noise power in each band is
updated
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primarily during silence while the noisy signal power is tracked at all times.
For each
frequency band, a gain (attenuation) factor is computed based on the SNR of
the band
and is used to attenuate the signal in the band. Thus, each frequency band of
the
noisy input speech signal is attenuated based on its SNR.
Figure 1B illustrates another more sophisticated prior approach using an
overall SNR level in addition to the individual SNR values to compute the gain
factors for each band. (See also reference [2].) The overall SNR is estimated
in the
Overall SNR Estimation block. The gain factor computations for each band are
performed in the Gain Computation block. The attenuation of the signals in
different
bands is accomplished by multiplying the signal in each band by the
corresponding
gain factor in the Gain Multiplication block. Low SNR bands are attenuated
more
than the high SNR bands. The amount of attenuation is also greater if the
overall
SNR is low. After the attenuation process, the signals in the different bands
are
recombined into a single, clean output signal. The resulting output signal
will have an
improved overall perceived quality.
The decomposition of the input noisy speech-containing signal can also be
performed using Fourier transform techniques or wavelet transform techniques.
Figure 2 shows the use of discrete Fourier transform techniques (shown as the
Windowing & FFT block). Here a block of input samples is transformed to the
frequency domain. The magnitude of the complex frequency domain elements are
attenuated based on the spectral subtraction principles described earlier. The
phase of
the complex frequency domain elements are left unchanged. The complex
frequency
domain elements are then transformed back to the time domain via an inverse
discrete
Fourier transform in the IFFT block, producing the output signal. Instead of
Fourier
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transform techniques, wavelet transform techniques may be used for decomposing
the
input signal.
A Voice Activity Detector is part of many noise suppression systems.
Generally, the power of the input signal is compared to a variable threshold
level.
Whenever the threshold is exceeded, speech is assumed to be present.
Otherwise, the
signal is assumed to contain only background noise. Such two-state voice
activity
detectors do not perform robustly under adverse conditions such as in cellular
telephony environments. An example of a voice activity detector is described
in
reference [5].
Various implementations of noise suppression systems utilizing spectral
subtraction differ mainly in the methods used for power estimation, gain
factor
determination, spectral decomposition of the input signal and voice activity
detection.
A broad overview of spectral subtraction techniques can be found in reference
[3].
Several other approaches to speech enhancement, as well as spectral
subtraction, are
overviewed in reference [4].
Perceptual spectral weighting can improve the performance of some adaptive
noise cancellation systems. In the past, deficiencies in weighting functions
have
limited the effectiveness of known noise cancellation systems. This invention
addresses and provides one solution for such problems.
BRIEF SUMMARY OF THE INVENTION
The preferred embodiment is useful in a communication system for processing
a communication signal including a speech component derived from speech and a
noise component derived from noise. In such an environment, the quality of the
communication signal can be enhanced by dividing the communication signal into
a
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plurality of frequency band signals representing the communication signal in a
plurality of frequency bands. The dividing may be accomplished with a filter
or a
calculator employing, for example, a Fourier transform. A control signal is
generated
in response to the speech component. The control signal indicates one or more
characteristics of the frequency distribution of the speech component
corresponding to
at least some of the frequency bands. Weighting values are assigned to the
frequency
band signals in response to the values of the control signal. The frequency
band
signals are altered in response to the weighting values to generate weighted
frequency
band signals. The weighted frequency band signals are combined to generate a
communication signal with enhanced quality.
The foregoing signal generation and manipulation of signals and values
preferably is accomplished with a calculator.
By using the foregoing techniques, a perceptual weighting function needed to
improve communication signal quality can be generated with a degree of ease
and
accuracy unattained by the known prior techniques.
BRIEF DESCRIPTION OF THE DRAWINGS
Figures 1A and 1B are schematic block diagrams of known noise cancellation
systems.
Figure 2 is a schematic block diagram of another form of a known noise
cancellation system.
Figure 3 is a functional and schematic block diagram illustrating a preferred
form of adaptive noise cancellation system made in accordance with the
invention.
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Figure 4 is a schematic block diagram illustrating one embodiment of the
invention implemented by a digital signal processor.
Figure 5 is graph of relative noise ratio versus weight illustrating a
preferred
assignment of weight for various ranges of values of relative noise ratios.
Figure 6 is a graph plotting power versus Hz illustrating a typical power
spectral density of background noise recorded from a cellular telephone in a
moving
vehicle.
Figure 7 is a curve plotting Hz versus weight obtained from a preferred form
of adaptive weighting function in accordance with the invention.
Figure 8 is a graph plotting Hz versus weight for a family of weighting curves
calculated according to a preferred embodiment of the invention.
Figure 9 is a graph plotting Hz versus decibels of the broad spectral shape of
a
typical voiced speech segment.
Figure 10 is a graph plotting Hz versus decibels of the broad spectral shape
of
a typical unvoiced speech segment.
Figure 11 is a graph plotting Hz versus decibels of perceptual spectral
weighting curves for ka=25.
Figure 12 is a graph plotting Hz versus decibels of perceptual spectral
weighting curves for ka=38.
Figure 13 is a graph plotting Hz versus decibels of perceptual spectral
weighting curves for k°=50.
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DESCRIPTION OF THE PREFERRED EMBODIIUVIENTS
The preferred form of ANC system shown in Figure 3 is robust under adverse
conditions often present in cellular telephony and packet voice networks. Such
adverse conditions include signal dropouts and fast changing background noise
conditions with wide dynamic ranges. The Figure 3 embodiment focuses on
attaining
high perceptual quality in the processed speech signal under a wide variety of
such
channel impairments.
The performance limitation imposed by commonly used two-state voice activity
detection functions is overcome in the preferred embodiment by using a
probabilistic
speech presence measure. This new measure of speech is called the Speech
Presence
Measure (SPM), and it provides multiple signal activity states and allows more
accurate handling of the input signal during different states. The SPM is
capable of
detecting signal dropouts as well as new environments. Dropouts are temporary
losses of the signal that occur commonly in cellular telephony and in voice
over
packet networks. New environment detection is the ability to detect the start
of new
calls as well as sudden changes in the background noise environment of an
ongoing
call. The SPM can be beneficial to any noise reduction function, including the
preferred embodiment of this invention.
Accurate noisy signal and noise power measures, which are performed for each
frequency band, improve the performance of the preferred embodiment. The
measurement for each band is optimized based on its frequency and the state
information from the SPM. The frequency dependence is due to the optimization
of
power measurement time constants based on the statistical distribution of
power
across the spectrum in typical speech and environmental background noise.
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Furthermore, this spectrally based optimization of the power measures has
taken into
consideration the non-linear nature of the human auditory system. The SPM
state
information provides additional information for the optimization of the time
constants
as well as ensuring stability and speed of the power measurements under
adverse
conditions. For instance, the indication of a new environment by the SPM
allows the
fast reaction of the power measures to the new environment.
According to the preferred embodiment, significant enhancements to perceived
quality, especially under severe noise conditions, are achieved via three
novel spectral
weighting functions. The weighting functions are based on (1) the overall
noise-to-
signal ratio (NSR), (2) the relative noise ratio, and (3) a perceptual
spectral weighting
model. The first function is based on the fact that over-suppression under
heavier
overall noise conditions provide better perceived quality. The second function
utilizes
the noise contribution of a band relative to the overall noise to
appropriately weight
the band, hence providing a fine structure to the spectral weighting. The
third
weighting function is based on a model of the power-frequency relationship in
typical
environmental background noise. The power and frequency are approximately
inversely related, from which the name of the model is derived. The inverse
spectral
weighting model parameters can be adapted to match the actual environment of
an
ongoing call. The weights are conveniently applied to the NSR values computed
for
each frequency band; although, such weighting could be applied to other
parameters
with appropriate modifications just as well. Furthermore, since the weighting
functions are independent, only some or all the functions can be jointly
utilized.
The preferred embodiment preserves the natural spectral shape of the speech
signal which is important to perceived speech quality. This is attained by
careful
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spectrally interdependent gain adjustment achieved through the attenuation
factors.
An additional advantage of such spectrally interdependent gain adjustment is
the
variance reduction of the attenuation factors.
Referring to Figure 3, a preferred form of adaptive noise cancellation system
10 made in accordance with the invention comprises an input voice channel 20
transmitting a communication signal comprising a plurality of frequency bands
derived from speech and noise to an input terminal 22. A speech signal
component of
the communication signal is due to speech and a noise signal component of the
communication signal is due to noise.
A filter function 50 filters the communication signal into a plurality of
frequency band signals on a signal path 51. A DTMF tone detection function 60
and
a speech presence measure function 70 also receive the communication signal on
input channel 20. The frequency band signals on path 51 are processed by a
noisy
signal power and noise power estimation function 80 to produce various forms
of
power signals.
The power signals provide inputs to an perceptual spectral weighting function
90, a relative noise ratio based weighting function 100 and an overall noise
to signal
ratio based weighting function 110. Functions 90, 100 and 110 also receive
inputs
from speech presence measure function 70 which is an improved voice activity
detector. Functions 90, 100 and 110 generate preferred forms of weighting
signals
having weighting factors for each of the frequency bands generated by filter
function
50. The weighting signals provide inputs to a noise to signal ratio
computation and
weighting function 120 which multiplies the weighting factors from functions
90, 100
and 110 for each frequency band together and computes an NSR value for each
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frequency band signal generated by the filter function 50. Some of the power
signals
calculated by function 80 also provide inputs to function 120 for calculating
the NSR
value.
Based on the combined weighting values and NSR value input from function
120, a gain computation and interdependent gain adjustment function 130
calculates
preferred forms of initial gain signals and preferred forms of modified gain
signals
with initial and modified gain values for each of the frequency bands and
modifies the
initial gain values for each frequency band by, for example, smoothing so as
to reduce
the variance of the gain. The value of the modified gain signal for each
frequency
band generated by function 130 is multiplied by the value of every sample of
the
frequency band signal in a gain multiplication function 140 to generate
preferred
forms of weighted frequency band signals. The weighted frequency band signals
are
summed in a combiner function 160 to generate a communication signal which is
transmitted through an output terminal 172 to a channel 170 with enhanced
quality. A
DTMF tone extension or regeneration function 150 also can place a DTMF tone on
channel 170 through the operation of combiner function 160.
The function blocks shown in Figure 3 may be implemented by a variety of
well known calculators, including one or more digital signal processors (DSP)
including a program memory storing programs which are executed to perform the
functions associated with the blocks (described later in more detail) and a
data
memory for storing the variables and other data described in connection with
the
blocks. One such embodiment is shown in Figure 4 which illustrates a
calculator in
the form of a digital signal processor 12 which communicates with a memory 14
over
a bus 16. Processor 12 performs each of the functions identified in connection
with
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the blocks of Figure 3. Alternatively, any of the function blocks may be
implemented
by dedicated hardware implemented by application specific integrated circuits
(ASICs), including memory, which are well known in the art. Of course, a
combination of one or more DSPs and one or more ASICs also may be used to
implement the preferred embodiment. Thus, Figure 3 also illustrates an ANC 10
comprising a separate ASIC for each block capable of performing the function
indicated by the block.
Filtering
In typical telephony applications, the noisy speech-containing input signal on
channel 20 occupies a 4kHz bandwidth. This communication signal may be
spectrally decomposed by filter 50 using a filter bank or other means for
dividing the
communication signal into a plurality of frequency band signals. For example,
the
filter function could be implemented with block-processing methods, such as a
Fast
Fourier Transform (FFT). In the case of an FFT implementation of filter
function 50,
the resulting frequency band signals typically represent a magnitude value (or
its
square) and a phase value. The techniques disclosed in this specification
typically are
applied to the magnitude values of the frequency band signals. Filter 50
decomposes
the input signal into N frequency band signals representing N frequency bands
on
path 51. The input to filter 50 will be denoted x(n) while the output of the
k'" filter
in the filter 50 will be denoted xk (n) , where n is the sample time.
The input, x(n) , to filter 50 is high-pass filtered to remove DC components
by
conventional means not shown.
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Gain Computation
We first will discuss one form of gain computation. Later, we will discuss an
interdependent gain adjustment technique. The gain (or attenuation) factor for
the k'''
frequency band is computed by function 130 once every T samples as
G . (n) = 1-W~ (n)NSRk (n) , n = O,T,2T ,... (1)
Gk (n -1) , n =1,2,...,T -1,T + 1,...,2T -1,...
A suitable value for T is 10 when the sampling rate is 8kHz. The gain factor
will
range between a small positive value, ~ , and 1 because the weighted NSR
values are
limited to lie in the range [0,1-~ ]. Setting the lower limit of the gain to ~
reduces the
effects of "musical noise" (described in reference [2]) and permits limited
background
signal transparency. In the preferred embodiment, ~ is set to 0.05. The
weighting
factor, W~ (n) , is used for over-suppression and under-suppression purposes
of the
signal in the k'" frequency band. The overall weighting factor is computed by
function 120 as
Wk (u) = u~ (n)vk (n)N'k (n) (2)
where uk (n) is the weight factor or value based on overall NSR as calculated
by
function 110, w~ (n) is the weight factor or value based on the relative noise
ratio
weighting as calculated by function 100, and v~ (n) is the weight factor or
value based
on perceptual spectral weighting as calculated by function 90. As previously
described, each of the weight factors may be used separately or in various
combinations.
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Gain Multiplication
The attenuation of the signal xA (n) from the k'h frequency band is achieved
by function 140 by multiplying xk (n) by its corresponding gain factor, Gk (n)
, every
sample to generate weighted frequency band signals. Combiner 160 sums the
resulting attenuated signals, y(n) , to generate the enhanced output signal on
channel
170. This can be expressed mathematically as:
Y(n) _ ~ Gk (n)xk (n) (
k
Power Estimation
The operations of noisy signal power and noise power estimation function 80
include the calculation of power estimates and generating preferred forms of
corresponding power band signals having power band values as identified in
Table 1
below. The power, P(n) at sample n, of a discrete-time signal u(n), is
estimated
approximately by either (a) lowpass filtering the full-wave rectified signal
or (b)
lowpass filtering an even power of the signal such as the square of the
signal. A first
order IIR filter can be used for the lowpass filter for both cases as follows:
P(n) _ /3P(n -1) + a ~ u(n) ~ (4a)
P(n) _ ~3P(n -1) + a[u(n)]Z (4b)
The lowpass filtering of the full-wave rectified signal or an even power of a
signal is
an averaging process. The power estimation (e.g., averaging) has an effective
time
window or time period during which the filter coefficients are large, whereas
outside
this window, the coefficients are close to zero. The coefficients of the
lowpass filter
determine the size of this window or time period. Thus, the power estimation
(e.g.,
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averaging) over different effective window sizes or time periods can be
achieved by
using different filter coefficients. When the rate of averaging is said to be
increased,
it is meant that a shorter time period is used. By using a shorter time
period, the
power estimates react more quickly to the newer samples, and "forget" the
effect of
older samples more readily. When the rate of averaging is said to be reduced,
it is
meant that a longer time period is used.
The first order IIR filter has the following transfer function:
H (z) = 1- ~z_, (5)
The DC gain of this filter is H(1) = 1 a~ . The coefficient, (3 , is a decay
constant.
The decay constant represents how long it would take for the present (non-
zero) value
of the power to decay to a small fraction of the present value if the input is
zero, i.e.
u(n) = 0 . If the decay constant, ~3 , is close to unity, then it will take a
longer time
for the power value to decay. If /3 is close to zero, then it will take a
shorter time for
the power value to decay. Thus, the decay constant also represents how fast
the old
power value is forgotten and how quickly the power of the newer input samples
is
incorporated. Thus, larger values of ~3 result in longer effective averaging
windows
or time periods.
Depending on the signal of interest, effectively averaging over a shorter or
longer time period may be appropriate for power estimation. Speech power,
which
has a rapidly changing profile, would be suitably estimated using a smaller ~3
. Noise
can be considered stationary for longer periods of time than speech. Noise
power
would be more accurately estimated by using a longer averaging window (large
~3 )
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The preferred form of power estimation significantly reduces computational
complexity by undersampling the input signal for power estimation purposes.
This
means that only one sample out of every T samples is used for updating the
power
P(n) in (4). Between these updates, the power estimate is held constant. This
procedure can be mathematically expressed as
P(n) - l3P(n -1) + a ~ u(n) ~ , n = 0,2T ,3T,... ( )
n =1,2,...T - l, T + 1,...2T -1,...
P(n -1) 6
Such first order lowpass IIR filters may be used for estimation of the various
power
measures listed in the Table 1 below:
Tahle 1
Variable Descri tion
Ps~c (n) Overall noisy signal power
peN (n) Overall background noise power
PS (n) Noisy signal power in the k'" frequency
band.
PN (n) Noise power in the k'h frequency band.
Pm.sT (n) Short-term overall noisy signal power
in the first
formant
Pm.r.T (n) Long-term overall noisy signal power
in the first
formant
Function 80 generates a signal for each of the foregoing Variables. Each of
the
signals in Table 1 is calculated using the estimations described in this Power
Estimation section. The Speech Presence Measure, which will be discussed
later,
utilizes short-term and long-term power measures in the first formant region.
To
perform the first formant power measurements, the input signal, x(n) , is
lowpass
b + b z-' + b z-'
filtered using an IIR filter H (z) = 1 + a, z ' + a~ z-'- ' In the preferred
implementation, the filter has a cut-off frequency at 850Hz and has
coefficients
bo = 0.1027 , b, = 0.2053 , a, _ -0.9754 and a, = 0.4103 . Denoting the output
of
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this filter as x~~,y, (n) , the short-term and long-term first formant power
measures can
be obtained as follows:
Plst.sT Vin) _ ~lsr,sT PlSr,sT O -1) + cxl~.r.sT xvw Vin) (7)
if Plst,LT O) < PlSt,sT Vin)
Plst,LT ~~) _ ~lst,LTaPIst,LT ~~ -1) +alst,LT,1 'xlox~ \n)
and DROPOUT = 0
if Plst,LT ~'~) ~ PlSt,sT O) 8
I''lst,LT.' Plst,LT ~n -1) + (xlst,LT,2 xmw ~~) and DROPOUT = 0 ( )
= PISt.LT (~ -1) if DROPOUT =1
DROPOUT in (8) will be explained later. The time constants used in the above
difference equations are the same as those described in (6) and are tabulated
below:
Time Constant Value
1/16000
alst.LT,1
//J) 15999/16000
i- IsI,LT,l
1/256
alst,LT,2
[~) 255/256
~lst,LT,'
1/128
alst,sT
~lst.sT 127/128
One effect of these time constants is that the snort term tirst rormant power
measure is
effectively averaged over a shorter time period than the long term first
formant power
measure. These time constants are examples of the parameters used to analyze a
communication signal and enhance its quality.
Noise-to-Signal Ratio (NSR) Estimation
Regarding overall NSR based weighting function 110, the overall NSR,
NSR~,Ve,av O) at sample n , is defined as
_ PaN (n)
NSRoverau ~~) - Psrc O)
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The overall NSR is used to influence the amount of over-suppression of the
signal in
each frequency band and will be discussed later. The NSR for the k'h frequency
band
may be computed as
NSR (n) = PN (n) (10)
k Ps (n)
Those skilled in the art recognize that other algorithms may be used to
compute the
NSR values instead of expression (10).
Speech Presence Measure (SPM)
Speech presence measure (SPM) 70 may utilize any known DTMF detection
method if DTMF tone extension or regeneration functions 150 are to be
performed.
In the preferred embodiment, the DTMF flag will be 1 when DTMF activity is
detected and 0 otherwise. If DTMF tone extension or regeneration is
unnecessary,
then the following can be understood by always assuming that DTMF=0.
SPM 70 primarily performs a measure of the likelihood that the signal activity
is
due to the presence of speech. This can be quantized to a discrete number of
decision
levels depending on the application. In the preferred embodiment, we use five
levels.
The SPM performs its decision based on the DTMF flag and the LEVEL value. The
DTMF flag has been described previously. The LEVEL value will be described
shortly. The decisions, as quantized, are tabulated below. The lower four
decisions
(Silence to High Speech) will be referred to as SPM decisions.
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Table 1: Joint Speech Presence Measure and DTMF Activity decisions
DTMF LEVEL Decision
1 X DTMF Activit Present
0 0 Silence Probabilit
0 1 Low S eech Probability
0 2 Medium S eech Probabilit
0 3 Hi h S eech Probabilit
In addition to the above multi-level decisions, the SPM also outputs two flags
or
signals, DROPOUT and NEWENV, which will be described in the following
sections.
Power Measurement in the SPM
The novel mufti-level decisions made by the SPM are achieved by using a
speech likelihood related comparison signal and multiple variable thresholds.
In our
preferred embodiment, we derive such a speech likelihood related comparison
signal
by comparing the values of the first formant short-term noisy signal power
estimate,
P~St,sT{~Z), and the first formant long-term noisy signal power estimate,
P~S~,LT(n).
Multiple comparisons are performed using expressions involving PIS_r,sT(n) and
Por,cz(rz) as given in the preferred embodiment of equation (11) below. The
result of
these comparisons is used to update the speech likelihood related comparison
signal.
In our preferred embodiment, the speech likelihood related comparison signal
is a
hangover counter, h~~ . Each of the inequalities involving Pls~,sT(n) and
P~Sr,LT(n) uses
different scaling values (i.e. the,u; 's). They also possibly may use
different additive
constants, although we use Po=2 for all of them.
The hangover counter, h~~ , can be assigned a variable hangover period that is
updated every sample based on multiple threshold levels, which, in the
preferred
embodiment, have been limited to 3 levels as follows:
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hvar = hmax,3 if P,S.I ST (n) ~ ~3~st,LT (~) + Po
= max[hmaX,z ~yl~;~ -1] if PlSt~sT (n) > ~zl'ut.~T (n) + Po
(11)
= max[hmaX,, , h~~ -1] if P,St,sT (n) > r'"1 ~ st,LT (n) + Po
= max[0, h~~ -1] otherwise
where hmax,3 > hmdX,z > hmaX,, and ,u3 > ,uz > ,u, .
Suitable values for the maximum values of h~~ are hmaX,3 = 2000 , hmaX,z =1400
and
hmax., = 800 . Suitable scaling values for the threshold comparison factors
are
,u3 = 3.0 , ,uz = 2.0 and ,u, =1.6 . The choice of these scaling values are
based on the
desire to provide longer hangover periods following higher power speech
segments.
Thus, the inequalities of (11) determine whether Plst~sT(n) exceeds P~SrLT(n)
by more
than a predetermined factor. Therefore, h~~ represents a preferred form of
comparison signal resulting from the comparisons defined in (11) and having a
value
representing differing degrees of likelihood that a portion of the input
communication
signal results from at least some speech.
Since longer hangover periods are assigned for higher power signal segments,
the hangover period length can be considered as a measure that is directly
proportional to the probability of speech presence. Since the SPM decision is
required
to reflect the likelihood that the signal activity is due to the presence of
speech, and
the SPM decision is based partly on the LEVEL value according to Table l, we
determine the value for LEVEL based on the hangover counter as tabulated
below.
Condition Decision
h~~ > hmdx,z LEVEL=3
LEVEL=2
h",3X,, > 12~~ > 0 LEVEL=1
h~;~ = 0 LEVEL=0
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SPM 70 generates a preferred form of a speech likelihood signal having values
corresponding to LEVELs 0-3. Thus, LEVEL depends indirectly on the power
measures and represents varying likelihood that the input communication signal
results from at least some speech. Basing LEVEL on the hangover counter is
advantageous because a certain amount of hysterisis is provided. That is, once
the
count enters one of the ranges defined in the preceding table, the count is
constrained
to stay in the range for variable periods of time. This hysterisis prevents
the LEVEL
value and hence the SPM decision from changing too often due to momentary
changes in the signal power. If LEVEL were based solely on the power measures,
the
SPM decision would tend to flutter between adjacent levels when the power
measures
lie near decision boundaries.
Dropout Detection in the SPM
Another novel feature of the SPM is the ability to detect 'dropouts' in the
signal. A dropout is a situation where the input signal power has a defined
attribute,
such as suddenly dropping to a very low level or even zero for short durations
of time
(usually less than a second). Such dropouts are often experienced especially
in a
cellular telephony environment. For example, dropouts can occur due to loss of
speech frames in cellular telephony or due to the user moving from a noisy
environment to a quiet environment suddenly. During dropouts, the ANC system
operates differently as will be explained later.
Dropout detection is incorporated into the SPM. Equation (8) shows the use of
a DROPOUT signal in the long-term (noise) power measure. During dropouts, the
adaptation of the long-term power for the SPM is stopped or slowed
significantly.
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This prevents the long-term power measure from being reduced drastically
during
dropouts, which could potentially lead to incorrect speech presence measures
later.
The SPM dropout detection utilizes the DROPOUT signal or flag and a
counter, Cdropnut ~ The counter is updated as follows every sample time.
Condition Decision/Action
Plst,ST (n) > dropout PlsI,GT Cdropout
(~) ~r Cdropout C2
Plst,ST -(~) ~ f~dropoutplst,LT Increment Cdrt~pttut
(~) and 0 C Cdrrspout ~ C,
The following table shows how DROPOUT should be updated.
Condition Decision/Action
O G Cdropout ~ c1 DROPOUT=1
Otherwise DROPOUT=0
As shown in the foregoing table, the attribute of cdropout determines at least
in part the
condition of the DROPOUT signal. A suitable value for the power threshold
comparison factor, tLldropout ~ is 0.2. Suitable values for c1 and c~ are c1 =
4000 and
c~ = 8000 , which correspond to 0.5 and 1 second, respectively. The logic
presented
here prevents the SPM from indicating the dropout condition for more than c1
samples.
Limiting_of Long-term (Noise) Power Measure in the SPM
In addition to the above enhancements to the long-term (noise) power
measure, Plst,LT (n) ~ it is further constrained from exceeding a certain
threshold,
put.GT.~x , i.e. if the value of Pl.,.t,LT (yi) computed according to equation
(7) is greater
than PI~t.LT.t"aX , then we set Pl,.t.GT (r2) = 1'ut,GT.~X . This enhancement
to the long-term
power measure makes the SPM more robust as it will not be able to rise to the
level of
the short-term power measure in the case of a long and continuous period of
loud
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speech. This prevents the SPM from providing an incorrect speech presence
measure
in such situations. A suitable value for PI~r,LT,",aX = 500 / 8159 assuming
that the
maximum absolute value of the input signal x(n) is normalized to unity.
New Environment Detection in the SPM
At the beginning of a call, the background noise environment would not be
known by ANC system 10. The background noise environment can also change
suddenly when the user moves from a noisy environment to a quieter environment
e.g. moving from a busy street to an indoor environment with windows and doors
closed. In both these cases, it would be advantageous to adapt the noise power
measures quickly for a short period of time. In order to indicate such changes
in the
environment, the SPM outputs a signal or flag called NEWENV to the ANC system.
The detection of a new environment at the beginning of a call will depend on
the system under question. Usually, there is some form of indication that a
new call
has been initiated. For instance, when there is no call on a particular line
in some
networks, an idle code may be transmitted. In such systems, a new call can be
detected by checking for the absence of idle codes. Thus, the method for
inferring
that a new call has begun will depend on the particular system.
In the preferred embodiment of the SPM, we use the flag NEWENV together
with a counter cneWe"v and a flag, OLDDROPOUT. The OLDDROPOUT flag
contains the value of the DROPOUT from the previous sample time.
A pitch estimator is used to monitor whether voiced speech is present in the
input signal. If voiced speech is present, the pitch period (i.e., the inverse
of pitch
frequency) would be relatively steady over a period of about 20ms. If only
background noise is present, then the pitch period would change in a random
manner.
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If a cellular handset is moved from a quiet room to a noisy outdoor
environment, the
input signal would be suddenly much louder and may be incorrectly detected as
speech. The pitch detector can be used to avoid such incorrect detection and
to set the
new environment signal so that the new noise environment can be quickly
measured.
To implement this function, any of the numerous known pitch period
estimation devices may be used, such as device 74 shown in Fig. 3. In our
preferred
implementation, the following method is used. Denoting K(n-T) as the pitch
period
estimate from T samples ago, and K(n) as the current pitch period estimate, if
~K(n)-
K(n-40) ~ >3, and ~K(n-40)-K(n-80) ~ >3, and ~K(n-80)-K(n-120) ~ >3, then the
pitch
period is not steady and it is unlikely that the input signal contains voiced
speech. If
these conditions are true and yet the SPM says that LEVEL>1 which normally
implies
that significant speech is present, then it can be inferred that a sudden
increase in the
background noise has occurred.
The following table specifies a method of updating NEWENV and cnewen,~
Condition Decision/Action
Beginning of a new call or NEWENV=1
( (OLDDROPOUT=1) and (DROPOUT=0) Cnewenv -O
) Or
(~K(n)-K(n-40)~>3 and ~K(n-40)-K(n-80)~>3
and
K(n-80)-K(n-120) >3 and LEVEL>1)
Not the beginning of a new call No action
or
OLDDROPOUT=0 or
DROPOUT=1
and NEWENV=1 Increment c
C newenv
~ ~
newenv.max
newenv
c = c NEWENV=0
newenv newenv,max
Cnewenv
In the above method, the NEWENV flag is set to 1 for a period of time
specified by
cnewenV.~x , after which it is cleared. The NEWENV flag is set to 1 in
response to
various events or attributes:
(1) at the beginning of a new call;
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(2) at the end of a dropout period;
(3) in response to an increase in background noise (for example, the pitch
detector 74 may reveal that a new high amplitude signal is not due to speech,
but
rather due to noise.); or
(4) in response to a sudden decrease in background noise to a lower level of
sufficient amplitude to avoid being a drop out condition.
A suitable value for the cnewenv,maX is 2000 which corresponds to 0.25
seconds.
Operation of the ANC System
Referring to Figure 3, the multi-level SPM decision and the flags DROPOUT
and NEWENV are generated on path 72 by SPM 70. With these signals, the ANC
system is able to perform noise cancellation more effectively under adverse
conditions. Furthermore, as previously described, the power measurement
function
has been significantly enhanced compared to prior known systems. Additionally,
the
three independent weighting functions carried out by functions 90, 100 and 110
can
be used to achieve over-suppression or under-suppression. Finally, gain
computation
and interdependent gain adjustment function 130 offers enhanced performance.
Use of Dropout Signals
When the flag DROPOUT=l, the SPM 70 is indicating that there is a
temporary loss of signal. Under such conditions, continuing the adaptation of
the
signal and noise power measures could result in poor behavior of a noise
suppression
system. One solution is to slow down the power measurements by using very long
time constants. In the preferred embodiment, we freeze the adaptation of both
signal
and noise power measures for the individual frequency bands, i.e. we set
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PN (n) = P,~ (n -1) and Ps (n) = Ps (n -1) when DROPOUT=1. Since DROPOUT
remains at 1 only for a short time (at most 0.5 sec in our implementation), an
erroneous dropout detection may only affect ANC system 10 momentarily. The
improvement in speech quality gained by our robust dropout detection outweighs
the
low risk of incorrect detection.
Use of New Environment SigLnals
When the flag NEWENV=1, SPM 70 is indicating that there is a new
environment due to either a new call or that it is a post-dropout environment.
If there
is no speech activity, i.e. the SPM indicates that there is silence, then it
would be
advantageous for the ANC system to measure the noise spectrum quickly. This
quick
reaction allows a shorter adaptation time for the ANC system to a new noise
environment. Under normal operation, the time constants, aN and ~3N , used for
the
noise power measurements would be as given in Table 2 below. When NEWENV=1,
we force the time constants to correspond to those specified for the Silence
state in
Table 2. The larger ~ values result in a fast adaptation to the background
noise power.
SPM 70 will only hold the NEWENV at 1 for a short period of time. Thus, the
ANC
system will automatically revert to using the normal Table 2 values after this
time.
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Table 2: Power measurement time constants
Time
Constants
SPM Frequency Range ~
N ~N S S
Decision
Silence Probability<800Hz or >2500HzT / 1- T / 0.533 1- T
LEVEL=0 60 6000 / 240
800Hz to 2500HzT / 1- T / 0.533 1- T
80 8000 / 240
Low Speech <800Hz or >2500HzT / 1- T / 0.533 1- T
Probability 120 12000 / 240
LEVEL=1 800Hz to 2500HzT / 1- T / 0.64 1- T
160 16000 / 200
<800Hz or >2500Hz 0.64 1- T
Medium Speech Noise / 200
power
values
Probability 800Hz to ZSOOHzremain 0.853 1- T
LEVEL=2 substantially / 150
constant.
High Speech <800Hz or >2500Hz 0.853 1- T
Probability / 150
LEVEL=3 800Hz to 2500Hz 1 1- T
/ 128
FrecLuency-Dependent and Speech Presence Measure-Based Time Constants for
Power Measurement
The noise and signal power measurements for the different frequency bands are
given
by
pN (~) - ~3~ PN (n -1) + aN ~ xk (n) ~ , n = 0,2T,3T,... (12)
pN (~ -1) , n =1,2,...T -1,T + 1,...2T -1,...
ps (n) _ ~3s ps (n -1) + cxs ~ x~. (n) ~ , n = 0,2T,3T,... ( )
ps (n -1) 13
n =1,2,...T -1, T + 1,...2T -1,...
In the preferred embodiment, the time constants ~3N , /3s , aN and as are
based on
both the frequency band and the SPM decisions. The frequency dependence will
be
explained first, followed by the dependence on the SPM decisions.
The use of different time constants for power measurements in different
frequency bands offers advantages. The power in frequency bands in the middle
of
the 4kHz speech bandwidth naturally tend to have higher average power levels
and
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variance during speech than other bands. To track the faster variations, it is
useful to
have relatively faster time constants for the signal power measures in this
region.
Relatively slower signal power time constants are suitable for the low and
high
frequency regions. The reverse is true for the noise power time constants,
i.e. faster
time constants in the low and high frequencies and slower time constants in
the
middle frequencies. We have discovered that it would be better to track at a
higher
speed the noise in regions where speech power is usually low. This results in
an
earlier suppression of noise especially at the end of speech bursts.
In addition to the variation of time constants with frequency, the time
constants are also based on the multi-level decisions of the SPM. In our
preferred
implementation of the SPM, there are four possible SPM decisions (i.e.,
Silence, Low
Speech, Medium Speech, High Speech). When the SPM decision is Silence, it
would
be beneficial to speed up the tracking of the noise in all the bands. When the
SPM
decision is Low Speech, the likelihood of speech is higher and the noise power
measurements are slowed down accordingly. The likelihood of speech is
considered
too high in the remaining speech states and thus the noise power measurements
are
turned off in these states. In contrast to the noise power measurement, the
time
constants for the signal power measurements are modified so as to slow down
the
tracking when the likelihood of speech is low. This reduces the variance of
the signal
power measures during low speech levels and silent periods. This is especially
beneficial during silent periods as it prevents short-duration noise spikes
from causing
the gain factors to rise.
In the preferred embodiment, we have selected the time constants as shown in
Table ? above. The DC gains of the IIR filters used for power measurements
remain
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fixed across all frequencies for simplicity in our preferred embodiment
although this
could be varied as well.
Weighting based on Overall NSR
In reference [2], it is explained that the perceived quality of speech is
improved by over-suppression of frequency bands based on the overall SNR. In
the
preferred embodiment, over-suppression is achieved by weighting the NSR
according
to (2) using the weight, u~ (n) , given by
u~ (n) = 0.5 + NSRoVe,Q~~ (n) (14)
Here, we have limited the weight to range from 0.5 to 1.5. This weight
computation
may be performed slower than the sampling rate for economical reasons. A
suitable
update rate is once per 2T samples.
Weighting Based on Relative Noise Ratios
We have discovered that improved noise cancellation results from weighting
based on relative noise ratios. According to the preferred embodiment, the
weighting,
denoted by wk , based on the values of noise power signals in each frequency
band,
has a nominal value of unity for all frequency bands. This weight will be
higher for a
frequency band that contributes relatively more to the total noise than other
bands.
Thus, greater suppression is achieved in bands that have relatively more
noise. For
bands that contribute little to the overall noise, the weight is reduced below
unity to
reduce the amount of suppression. This is especially important when both the
speech
and noise power in a band are very low and of the same order. In the past, in
such
situations, power has been severely suppressed, which has resulted in hollow
sounding speech. However, with this weighting function, the amount of
suppression
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is reduced, preserving the richness of the signal, especially in the high
frequency
region.
There are many ways to determine suitable values for w~ . First, we note that
the average background noise power is the sum of the background noise powers
in N
frequency bands divided by the N frequency bands and is represented by PBN
(rz) l N .
The relative noise ratio in a frequency band can be defined as
_ P~'(~) (15)
PaN (~)l N
The goal is to assign a higher weight for a band when the ratio, R~ (n) , for
that
band is high, and lower weights when the ratio is low. In the preferred
embodiment,
we assign these weights as shown in Figure 5, where the weights are allowed to
range
between 0.5 and 2. To save on computational time and cost, we perform the
update of
(15) once per 2T samples. Function 80 (Figure 3) generates preferred forms of
band
power signals corresponding to the terms on the right side of equation (15)
and
function 100 generates preferred forms of weighting signals with weighting
values
corresponding to the term on the left side of equation (15).
If an approximate knowledge of the nature of the environmental noise is
known, then the RNR weighting technique can be extended to incorporate this
knowledge. Figure 6 shows the typical power spectral density of background
noise
recorded from a cellular telephone in a moving vehicle. Typical environmental
background noise has a power spectrum that corresponds to pink or brown noise.
(Pink noise has power inversely proportional to the frequency. Brown noise has
power inversely proportional to the square of the frequency.) Based on this
approximate knowledge of the relative noise ratio profile across the frequency
bands,
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the perceived quality of speech is improved by weighting the lower frequencies
more
heavily so that greater suppression is achieved at these frequencies.
We take advantage of the knowledge of the typical noise power spectrum
profile (or equivalently, the RNR profile) to obtain an adaptive weighting
function. In
general, the weight, ivf for a particular frequency, f , can be modeled as a
function
of frequency in many ways. One such model is
~'f =b(f -fo)u +c (16)
This model has three parameters { b, fo,c }. An example of a weighting curve
obtained from this model is shown in Figure 7 for b = 5.6 x 10-g , fo = 3000
and
c = 0.5 .
The Figure 7 curve varies monotonically with decreasing values of weight from
0 Hz
to about 3000 Hz, and also varies monotonically with increasing values of
weight
from about 3000 Hz to about 4000 Hz. In practice, we could use the frequency
band
index, k , corresponding to the actual frequency f . This provides the
following
practical and efficient model with parameters { b, ko , c }
w~ =b(k-ka)' +c (17)
In general, the ideal weights, w~ , may be obtained as a function of the
measured noise
power estimates, PN , at each frequency band as follows:
w . = min(1,
PN k ) (1g)
max{ PN }
k
Basically, the ideal weights are equal to the noise power measures normalized
by the
largest noise power measure. In general, the normalized power of a noise
component
in a particular frequency band is defined as a ratio of the power of the noise
component in that frequency band and a function of some or all of the powers
of the
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noise components in the frequency band or outside the frequency band.
Equations
(15) and (18) are examples of such normalized power of a noise component. In
case
all the power values are zero, the ideal weight is set to unity. This ideal
weight is
actually an alternative definition of RNR. We have discovered that noise
cancellation
can be improved by providing weighting which at least approximates normalized
power of the noise signal component of the input communication signal. In the
preferred embodiment, the normalized power may be calculated according to
(18).
Accordingly, function 100 (Figure 3) may generate a preferred form of
weighting
signals having weighting values approximating equation (18).
The approximate model in (17) attempts to mimic the ideal weights computed
using ( 18). To obtain the model parameters { b, ko , c } , a least-squares
approach may
be used. An efficient way to perform this is to use the method of steepest
descent to
adapt the model parameters { b, ko , c } .
We derive here the general method of adapting the model parameters using the
steepest descent technique. First, the total squared error between the weights
generated by the model and the ideal weights is defined for each frequency
band as
follows:
e2=~b(k-ko)Z+c-w~ ~ (19)
Taking the partial derivative of the total squared error, e' , with respect to
each of the
model parameters in turn and dropping constant terms, we obtain
z
_ae - ~ [b(k - ko ) - + c - w~ ~(k - ko )' (20)
ab
ae- --~~b(k-ko)~+c-wk~h(k-ko) (21)
ago ~L,~L~
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ae' -~~b(k-ko)z+c_y~,k~
(22)
7c ~L, rk
Denoting the model parameters and the error at the n'h sample time as { bn ,
ko,n > cn }
and en (k) , respectively, the model parameters at the (n + 1)'" sample can be
estimated
as
bn+~ = b-~ - ~U 7b~ (23)
ae'
ko,n+~ = ko,n - ~k ak (24)
O,n
a~ ae~ ( )
Cn+~ = Cn - "c 7cn 25
Here { ~,~ , ~.k , ~,~ } are appropriate step-size parameters. The model
definition in ( 17)
can then be used to obtain the weights for use in noise suppression, as well
as being
used for the next iteration of the algorithm. The iterations may be performed
every
sample time or slower, if desired, for economy.
We have described the alternative preferred RNR weight adaptation technique
above. The weights obtained by this technique can be used to directly multiply
the
corresponding NSR values. These are then used to compute the gain factors for
attenuation of the respective frequency bands.
In another embodiment, the weights are adapted efficiently using a simpler
adaptation technique for economical reasons. We fix the value of the weighting
model parameter ko to ko = 36 which corresponds to f~ = 2880Hz in (16).
Furthermore, we set the model parameter bn at sample time n to be a function
of ko
and the remaining model parameter cn as follows:
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bn = 1 k cn (26)
0
Equation (26) is obtained by setting k = 0 and wk =1 in (17). We adapt only cn
to
determine the curvature of the relative noise ratio weighting curve. The range
of cn is
restricted to [0.1,1.0]. Several weighting curves corresponding to these
specifications
are shown in Figure 8. Lower values of cn correspond to the lower curves. When
cn =1, no spectral weighting is performed as shown in the uppermost line. For
all
other values of c" , the curves vary monotonically in the same manner
described in
connection with Figure 7. The greatest amount of curvature is obtained when
c,1 = 0.1 as shown in the lowest curve. The applicants have found it
advantageous to
arrange the weighting values so that they vary monotonically between two
frequencies separated by a factor of 2 (e.g., the weighting values vary
monotonically
between 1000-2000 Hz and/or between 1500-3000 Hz).
The determination of cn is performed by comparing the total noise power in
the lower half of the signal bandwidth to the total noise power in the upper
half. We
define the total noise power in the lower and upper half bands as:
_ k
~atal,lower (n) ~ pN (n) (27)
kE F
uwPr
Ptotal,upper (n) ~ pN (~) (2g)
kE uvcPr
Alternatively, lowpass and highpass filter could be used to filter x(~a)
followed by
appropriate power measurement using (6) to obtain these noise powers. In our
filter
bank implementation, k E {3,4,...,42} and hence Fhw,fr = {3,4,...22} and
F ppe,. _ {23,24,...42} . Although these power measures may be updated every
sample,
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they are updated once every 2T samples for economical reasons. Hence the value
of
cn needs to be updated only as often as the power measures. It is defined as
follows:
~atal,upper (yZ)
cn = max min ,1.0 ,0.1 (29)
~otal,lower (n)
The min and max functions restrict c" to lie within [0.1,1.0].
According to another embodiment, a curve, such as Figure 7, could be stored
as a weighting signal or table in memory 14 and used as static weighting
values for
each of the frequency band signals generated by filter 50. The curve could
vary
monotonically, as previously explained, or could vary according to the
estimated
spectral shape of noise or the estimated overall noise power, PBN (n) ,as
explained in
the next paragraphs.
Alternatively, the power spectral density shown in Figure 6 could be thought
of as defining the spectral shape of the noise component of the communication
signal
received on channel 20. The value of c is altered according to the spectral
shape in
order to determine the value of w~ in equation (17). Spectral shape depends on
the
power of the noise component of the communication signal received on channel
20.
As shown in equations (12) and (13), power is measured using time constants aN
and
/3N which vary according to the likelihood of speech as shown in Table 2.
Thus, the
weighting values determined according to the spectral shape of the noise
component
of the communication signal on channel 20 are derived in part from the
likelihood that
the communication signal is derived at least in part from speech.
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According to another embodiment, the weighting values could be determined
from the overall background noise power. In this embodiment, the value of c in
equation (17) is determined by the value of PBN (n) .
In general, according to the preceding paragraphs, the weighting values may
vary in accordance with at least an approximation of one or more
characteristics (e.g.,
spectral shape of noise or overall background power) of the noise signal
component of
the communication signal on channel 20.
Perceptual Spectral Wei~htin~
We have discovered that improved noise cancellation results from perceptual
spectral weighting (PSW) in which different frequency bands are weighted
differently
based on their perceptual importance. Heavier weighting results in greater
suppression in a frequency band. For a given SNR (or NSR), frequency bands
where
speech signals are more important to the perceptual quality are weighted less
and
hence suppressed less. Without such weighting, noisy speech may sometimes
sound
'hollow' after noise reduction. Hollow sound has been a problem in previous
noise
reduction techniques because these systems had a tendency to oversuppress the
perceptually important parts of speech. Such oversuppression was partly due to
not
taking into account the perceptually important spectral interdependence of the
speech
signal.
The perceptual importance of different frequency bands change depending on
characteristics of the frequency distribution of the speech component of the
communication signal being processed. Determining perceptual importance from
such characteristics may be accomplished by a variety of methods. For example,
the
characteristics may be determined by the likelihood that a communication
signal is
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derived from speech. As explained previously, this type of classification can
be
implemented by using a speech likelihood related signal, such as h~;~ .
Assuming a
signal was derived from speech, the type of signal can be further classified
by
determining whether the speech is voiced or unvoiced. Voiced speech results
from
vibration of vocal cords and is illustrated by utterance of a vowel sound.
Unvoiced
speech does not require vibration of vocal cords and is illustrated by
utterance of a
consonant sound.
The broad spectral shapes of typical voiced and unvoiced speech segments are
shown in Figures 9 and 10, respectively. Typically, the 1000Hz to 3000Hz
regions
contain most of the power in voiced speech. For unvoiced speech, the higher
frequencies (>2500Hz) tend to have greater overall power than the lower
frequencies.
The weighting in the PSW technique is adapted to maximize the perceived
quality as
the speech spectrum changes.
As in RNR weighting technique, the actual implementation of the perceptual
spectral weighting may be performed directly on the gain factors for the
individual
frequency bands. Another alternative is to weight the power measures
appropriately.
In our preferred method, the weighting is incorporated into the NSR measures.
The PSW technique may be implemented independently or in any combination
with the overall NSR based weighting and RNR based weighting methods. In our
preferred implementation, we implement PSW together with the other two
techniques
as given in equation (2).
The weights in the PSW technique are selected to vary between zero and one.
Larger weights correspond to greater suppression. The basic idea of PSW is to
adapt
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the weighting curve in response to changes in the characteristics of the
frequency
distribution of at least some components of the communication signal on
channel 20.
For example, the weighting curve may be changed as the speech spectrum changes
when the speech signal transitions from one type of communication signal to
another,
e.g., from voiced to unvoiced and vice versa. In some embodiments, the
weighting
curve may be adapted to changes in the speech component of the communication
signal. The regions that are most critical to perceived quality (and which are
usually
oversuppressed when using previous methods) are weighted less so that they are
suppressed less. However, if these perceptually important regions contain a
significant amount of noise, then their weights will be adapted closer to one.
Many weighting models can be devised to achieve the PSW. In a manner similar
to the RNR technique's weighting scheme given by equation (17), we utilize the
practical and efficient model with parameters {b, ko, c}
v~ = b(k - ko )2 + c (30)
Here vA is the weight for frequency band k. In this method, we will vary only
k0
and c. This weighting curve is generally U-shaped and has a minimum value of c
at
frequency band ko . For simplicity, we fix the weight at k=0 to unity. This
gives the
following equation for b as a function of ko and c:
b - lk~c (31)
0
The lowest weight frequency band, ko , is adapted based on the likelihood of
speech being voiced or unvoiced. In our preferred method, ko is allowed to be
in the
range [25,50], which corresponds to the frequency range [2000Hz, 4000Hz].
During
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strong voiced speech, it is desirable to have the U-shaped weighting curve v~
to have
the lowest weight frequency band ko to be near 2000Hz. This ensures that the
midband frequencies are weighted less in general. During unvoiced speech, the
lowest weight frequency band ko is placed closer to 4000Hz so that the mid to
high
frequencies are weighted less, since these frequencies contain most of the
perceptually
important parts of unvoiced speech. To achieve this, the lowest weight
frequency
band ko is varied with the speech likelihood related comparison signal which
is the
hangover counter, h~~ , in our preferred method. Recall that h~~ is always in
the
range [0, hmaX,s =2000]. Larger values of h~;~ indicate higher likelihoods of
speech and
also indicate a higher likelihood of voiced speech. Thus, in our preferred
method, the
lowest weight frequency band is varied with the speech likelihood related
comparison
signal as follows:
ko = L50 - hear /80 J (32)
Since ko is an integer, the floor function L~ is used for rounding.
Next, the method for adapting the minimum weight c is presented. In one
approach, the minimum weight c could be fixed to a small value such as 0.25.
However, this would always keep the weights in the neighborhood of the lowest
weight frequency band ko at this minimum value even if there is a strong noise
component in that neighborhood. This could possibly result in insufficient
noise
attenuation. Hence we use the novel concept of a regional NSR to adapt the
minimum
weight.
The regional NSR, NSR,eR,~,n~~ (k) , is defined with respect to the minimum
weight
frequency band ko and is given by:
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PN (n)
kE~ko-z,k~+z~ 33
NSRre~~onal (n) _ ~ Ps (n) ( )
kE[ko-2,ko+zJ
Basically, the regional NSR is the ratio of the noise power to the noisy
signal
power in a neighborhood of the minimum weight frequency band ko . In our
preferred
method, we use up to 5 bands centered at ko as given in the above equation.
In our preferred implementation, when the regional NSR is -lSdB or lower, we
set
the minimum weight c to 0.25 (which is about l2dB). As the regional NSR
approaches its maximum value of OdB, the minimum weight is increased towards
unity. This can be achieved by adapting the minimum weight c at sample time n
as
0.25 , NSRaverau (n) < 0.1778 = -lSdB
c 0.912NSR (n) + 0.088 , 0.1778 <- NSR (n) << 1 (34)
overall overall
The vk curves are plotted for a range of values of c and ko in Figures 11-13
to
illustrate the flexibility that this technique provides in adapting the
weighting curves.
Regardless of ko, the curves are flat when c=1, which corresponds to the
situation
where the regional NSR is unity (OdB). The curves shown in Figures 11-13 have
the
same monotonic properties and may be stored in memory 14 as a weighting signal
or
table in the same manner previously described in connection with Figure 7.
As can be seen from equation (32), processor 12 generates a control signal
from
the speech likelihood signal h~~ which represents a characteristic of the
speech and
noise components of the communication signal on channel 20. As previously
explained, the likelihood signal can also be used as a measure of whether the
speech is
voiced or unvoiced. Determining whether the speech is voiced or unvoiced can
be
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accomplished by means other than the likelihood signal. Such means are known
to
those skilled in the field of communications.
The characteristics of the frequency distribution of the speech component of
the
channel 20 signal needed for PSW also can be determined from the output of
pitch
estimator 74. In this embodiment, the pitch estimate is used as a control
signal which
indicates the characteristics of the frequency distribution of the speech
component of
the channel 20 signal needed for PSW. The pitch estimate, or to be more
specific, the
rate of change of the pitch, can be used to solve for ko in equation (32). A
slow rate
of change would correspond to smaller ko values, and vice versa.
In one embodiment of PSW, the calculated weights for the different bands are
based on an approximation of the broad spectral shape or envelope of the
speech
component of the communication signal on channel 20. More specifically, the
calculated weighting curve has a generally inverse relationship to the broad
spectral
shape of the speech component of the channel 20 signal. An example of such an
inverse relationship is to calculate the weighting curve to be inversely
proportional to
the speech spectrum, such that when the broad spectral shape of the speech
spectrum
is multiplied by the weighting curve, the resulting broad spectral shape is
approximately flat or constant at all frequencies in the frequency bands of
interest.
This is different from the standard spectral subtraction weighting which is
based on
the noise-to-signal ratio of individual bands. In this embodiment of PSW, we
are
taking into consideration the entire speech signal (or a significant portion
of it) to
determine the weighting curve for all the frequency bands. In spectral
subtraction, the
weights are determined based only on the individual bands. Even in a spectral
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subtraction implementation such as in Figure 1B, only the overall SNR or NSR
is
considered but not the broad spectral shape.
Computation of Broad Spectral Shape or Envelope of Speech
There are many methods available to approximate the broad spectral shape of
the speech component of the channel 20 signal. For instance, linear prediction
analysis techniques, commonly used in speech coding, can be used to determine
the
spectral shape.
Alternatively, if the noise and signal powers of individual frequency bands
are
tracked using equations such as (12) and (13), the speech spectrum power at
the k"'
band can be estimated as [Ps (n)-PN (n)]. Since the goal is to obtain the
broad
spectral shape, the total power, Ps (n) , may be used to approximate the
speech power
in the band. This is reasonable since, when speech is present, the signal
spectrum
shape is usually dominated by the speech spectrum shape. The set of band power
values together provide the broad spectral shape estimate or envelope
estimate. The
number of band power values in the set will vary depending on the desired
accuracy
of the estimate. Smoothing of these band power values using moving average
techniques is also beneficial to remove jaggedness in the envelope estimate.
Computation of Perceptual Spectral Weighting Curve
After the broad spectral shape is approximated, the perceptual weighting curve
may be determined to be inversely proportional to the broad spectral shape
approximation. For instance, if Ps (n) is used as the broad spectral shape
estimate at
the k~'' band, then the weight for the k''' band, vA , may be determined as
vA (n) _ ~ l Ps (n) , where t~ is a predetermined value. In this embodiment, a
set of
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speech power values, such as a set of Ps (n) values, is used as a control
signal
indicating the characteristics of the frequency distribution of the speech
component of
the channel 20 signal needed for PSW. By using the foregoing spectral shape
estimate and weighting curve, the variation of the power signals used for the
estimate
is reduced across the N frequency bands. For instance, the spectrum shape of
the
speech component of the channel 20 signal is made more nearly flat across the
N
frequency bands, and the variation in the spectrum shape is reduced.
For economical reasons, we use a parametric technique in our preferred
implementation which also has the advantage that the weighting curve is always
smooth across frequencies. We use a parametric weighting curve, i.e. the
weighting
curve is formed based on a few parameters that are adapted based on the
spectral
shape. The number of parameters is less than the number of weighting factors.
The
parametric weighting function in our economical implementation is given by the
equation (30), which is a quadratic curve with three parameters.
Use of Wei~htin~ Functions
Although we have implemented weighting functions based on overall NSR
( uA ), perceptual spectral weighting ( vA ) and relative noise ratio
weighting ( wk )
jointly, a noise cancellation system will benefit from the implementation of
only one
or various combinations of the functions.
In our preferred embodiment, we implement the weighting on the NSR values
for the different frequency bands. One could implement these weighting
functions
just as well, after appropriate modifications, directly on the gain factors.
Alternatively, one could apply the weights directly to the power measures
prior to
computation of the noise-to-signal values or the gain factors. A further
possibility is
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to perform the different weighting functions on different variables
appropriately in the
ANC system. Thus, the novel weighting techniques described are not restricted
to
specific implementations.
Spectral Smoothing,_and Gain Variance Reduction Across Frequency Bands
In some noise cancellation applications, the bandpass filters of the filter
bank
used to separate the speech signal into different frequency band components
have little
overlap. Specifically, the magnitude frequency response of one filter does not
significantly overlap the magnitude frequency response of any other filter in
the filter
bank. This is also usually true for discrete Fourier or fast Fourier transform
based
implementations. In such cases, we have discovered that improved noise
cancellation
can be achieved by interdependent gain adjustment. Such adjustment is affected
by
smoothing of the input signal spectrum and reduction in variance of gain
factors
across the frequency bands according to the techniques described below. The
splitting of the speech signal into different frequency bands and applying
independently determined gain factors on each band can sometimes destroy the
natural spectral shape of the speech signal. Smoothing the gain factors across
the
bands can help to preserve the natural spectral shape of the speech signal.
Furthermore, it also reduces the variance of the gain factors.
This smoothing of the gain factors, G~ (n) (equation (1)), can be performed by
modifying each of the initial gain factors as a function of at least two of
the initial
gain factors. The initial gain factors preferably are generated in the form of
signals
with initial gain values in function block 130 (Figure 3) according to
equation (1).
According to the preferred embodiment, the initial gain factors or values are
modified
using a weighted moving average. The gain factors corresponding to the low and
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high values of k must be handled slightly differently to prevent edge effects.
The
initial gain factors are modified by recalculating equation (1) in function
130 to a
preferred form of modified gain signals having modified gain values or
factors. Then
the modified gain factors are used for gain multiplication by equation (3) in
function
block 140 (Figure 3).
More specifically, we compute the modified gains by first computing a set of
initial gain values, Gk (n) . We then perform a moving average weighting of
these
initial gain factors with neighboring gain values to obtain a new set of gain
values,
Gk (n) . The modified gain values derived from the initial gain values is
given by
k,
Gk (n) _ ~MkGk (h) (35)
k=k~
The Mk are the moving average coefficients tabulated below for our preferred
embodiment.
Range of Moving Average WeightingFirst coefficient
k Coefficients, Mk to
be multiplied
with
k = 3 0.95, 0.04, 0.01 G3 (n)
k = 4 0.02, 0.95, 0.02, 0.01 G3 (n)
5 <_ k 0.005, 0.02, 0.95, 0.02,Gk_Z (n)
<_ 40 0.005
k = 41 0.01, 0.02, 0.95, 0.02 G39 (n)
k = 42 0.01, 0.04, 0.95 G4o (n)
We have discovered that improved noise cancellation is possible with
coefficients selected from the following ranges of values. One of the
coefficients is in
the range of 10 to 50 times the value of the sum of the other coefficients.
For
example, the coefficient 0.95 is in the range of 10 to 50 times the value of
the sum of
the other coefficients shown in each line of the preceding table. More
specifically, the
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coefficient 0.95 is in the range from .90 to .98. The coefficient 0.05 is in
the range .02
to .09.
In another embodiment, we compute the gain factor for a particular frequency
band as a function not only of the corresponding noisy signal and noise
powers, but
also as a function of the neighboring noisy signal and noise powers. Recall
equation
(1):
1-Wk (n)NSRk (n) , n = O,T,2T,...
Gk (n) _ (1)
Gk (n-1) , n=1,2,...,T-1,T+1,...,2T-1,...
In this equation, the gain for frequency band k depends on NSRk (n) which in
turn
depends on the noise power, PN (n) , and noisy signal power, Ps (n) of the
same
frequency band. We have discovered an improvement on this concept whereby
Gk (n) is computed as a function noise power and noisy signal power values
from
multiple frequency bands. According to this improvement, Gk (n) may be
computed
using one of the following methods:
k° , n = O,T,2T,...
Gk (n) - 1-Wk (n)~MkNSRk (n) (1.l)
k=k~
Gk (n-1) , n=1,2,...,T-1,T+1,...,2T-1,...
k,
~MkPN(n) , n=O,T,2T,...
k k k~ k (1.2)
Gk (r~) -_ 1 W (n) Ps (n)
Gk (n-1) ~ n=1,2,...,T-1,T+1,...,2T-1,...
_ PN (n) , n = O,T,2T,...
1 Wk (n) k,
Gk (n) _ ~MkPsk (n) (1.3)
k=k.
Gk (n -1) , n =1,2,...,T -1,T + 1,...,2T -1,...
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k,
k
~,MkPN(n) , n=0,T,2T,...
1 Wk (n) k~
k k (1.4)
Gk (n) = k
~,MkPs (n)
k=k, , n =1,2,...,T -1,T +1,...,2T-1,...
Gk (n -1)
Our preferred embodiment uses equation (1.4) with Mk determined using the same
table given above.
Methods described by equations ( 1.1 )-( 1.4) all provide smoothing of the
input
signal spectrum and reduction in variance of the gain factors across the
frequency
bands. Each method has its own particular advantages and trade-offs. The first
method (1.1) is simply an alternative to smoothing the gains directly.
The method of (1.2) provides smoothing across the noise spectrum only while
(1.3) provides smoothing across the noisy signal spectrum only. Each method
has its
advantages where the average spectral shape of the corresponding signals are
maintained. By performing the averaging in (1.2), sudden bursts of noise
happening in
a particular band for very short periods would not adversely affect the
estimate of the
noise spectrum. Similarly in method (1.3), the broad spectral shape of the
speech
spectrum which is generally smooth in nature will not become too jagged in the
noisy
signal power estimates due to, for instance, changing pitch of the speaker.
The
method of (1.4) combines the advantages of both (1.2) and (1.3).
There is a subtle difference between (1.4) and (1.1). In (1.4), the averaging
is
performed prior to determining the NSR ratio. In (1.1), the NSR values are
computed
first and then averaged. Method (1.4) is computationally more expensive than
(1.1)
but performs better than (1.1).
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References
[1] IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 28, No.
2,
Apr. 1980, pp. 137-145, "Speech Enhancement Using a Soft-Decision Noise
Suppression Filter", Robert J. McAulay and Marilyn L. Malpass.
[2] IEEE Conference on Acoustics, Speech and Signal Processing, April 1979,
pp.
208-211, "Enhancement of Speech Corrupted by Acoustic Noise", M. Berouti, R.
Schwartz and J. Makhoul.
[3] Advanced Signal Processing and Digital Noise Reduction, 1996, Chapter 9,
pp.
242-260, Saeed V. Vaseghi. (ISBN Wiley 0471958751)
[4] Proceedings of the IEEE, Vol. 67, No. 12, December 1979, pp. 1586-1604,
"Enhancement and Bandwidth Compression of Noisy Speech", Jake S. Lim and
Alan V. Oppenheim.
[5] U.S. Patent 4,351,983, "Speech detector with variable threshold", Sep. 28,
1982.
William G. Crouse, Charles R. Knox.
Those skilled in the art will recognize that preceding detailed description
discloses
the preferred embodiments and that those embodiments may be altered and
modified
without departing from the true spirit and scope of the invention as defined
by the
accompanying claims. For example, the numerators and denominators of the
ratios
shown in this specification could be reversed and the shape of the curves
shown in
Figures 5, 7 and 8 could be reversed by making other suitable changes in the
algorithms. In addition, the function blocks shown in Figure 3 could be
implemented
in whole or in part by application specific integrated circuits or other forms
of logic
circuits capable of performing logical and arithmetic operations.
