 |
Description  |
|
|
BACKGROUND OF THE INVENTION
1. Field of Invention
The invention relates to voice detection technology, and more particularly
to estimation of noise floors to aid in voice discrimination.
2. Description of Related Art
Voice Activity Detectors (VADs) are an important component in speech coding
systems which make use of the natural silence periods in the speech signal
to increase transmission efficiency. They are also an essential part of
most speech enhancement systems, since in these systems the input noise
level and spectral shape are typically measured and updated in only those
segments which contain noise only.
VAD information is useful in other applications as well, such as
streamlining speech packets on the Internet by compensating for network
delays at gaps in speech activity, or detecting end points of speech
utterances under noisy conditions in speech recognition tasks.
In most of these applications the background noise is not always
stationary. In a hands-free mobile telephone system for instance both car
and road noise may change quickly. The VAD therefore has to adapt quickly
to the varying noise conditions to provide an accurate indication of
noise-only segments. Since the speech signal itself is also not
stationary, this task is usually not a simple one. Several VAD algorithms
and adaptation methods have been reported in recent years, some of them
being part (or in the process of being standardized as part) of standard
speech coding systems known in the art. However, these VADs are
complicated, and leave room for improvements, both in terms of performance
and complexity, particularly for applications other than speech coding.
SUMMARY OF THE INVENTION
The invention overcoming these and other problems in the art relates to a
system and method for noise threshold adaptation for voice detection based
in part on the observation that the background noise level can be updated
even during short silence intervals in the speech signal, by tracking a
parameter termed a "lower envelope" of the input signal. For simplicity
the invention is described as part of a low-complexity time-domain VAD,
which is found to work well down to SNR values of about 0 dB. It will
however be understood that the invention can be embedded in more complex
VADs capable of providing good performance even at lower SNR values.
BRIEF DESCRIPTION OF THE DRAWINGS
The invention will be described with reference to the following drawings,
in which like elements are designated by like numbers and in which:
FIG. 1 illustrates a schematic block diagram of a VAD system according to
the invention;
FIG. 2 illustrates use of the power stationarity test during a helicopter
noise transition;
FIG. 3 illustrates a helicopter noise transition wave form with
superimposed VAD decisions;
FIG. 4 illustrates the use of a lower envelope to update the noise
threshold according to the invention;
FIG. 5 illustrates the wave form of two spoken sentences in a white noise
ramp with superimposed VAD decisions according to the invention;
FIG. 6 illustrates the combination of the power stationarity test with
lower envelope tracking according to the invention;
FIG. 7 illustrates a flowchart of lower envelope and noise threshold
generation according to the invention;
FIG. 8 illustrates VAD output for tape hiss transition followed by music
and speech according to the invention;
FIG. 9 illustrates a waveform of tape hiss transition followed by the onset
of music and speech according to the invention with superimposed VAD
decisions according to the invention;
FIG. 10 illustrates VAD output for spoken sentences in car noise according
to the invention;
FIG. 11 illustrates a waveform of six sentences in car noise with
superimposed VAD decisions according to the invention;
FIG. 12 illustrates VAD output for isolated spoken words in helicopter
noise according to the invention;
FIG. 13 illustrates the waveform of isolated spoken words in helicopter
noise with superimposed VAD decisions according to the invention;
FIG. 14 illustrates VAD output for six spoken sentences in white noise
according to the invention; and
FIG. 15 illustrates a waveform of six spoken sentences in white noise with
superimposed VAD decisions according to the invention.
DETAILED DESCRIPTION OF THE DRAWINGS
A. Low Complexity Time-Domain VAD with which the Invention Illustratively
Operates
To demonstrate the system and method of the invention a low complexity time
domain VAD implementation is first described, in conjunction with which
the invention operates, as illustrated in FIG. 1. VAD 20 includes a
processor 80 connected to electronic memory 90 and hard disk storage 100
on which is stored control program 120 to carry out computational and
other aspects of the invention. VAD 20 is connected to an input unit 70
which may be a microphone or other source of input signals, and to output
unit 110 which may include an audible output unit or digital signal
processing or other circuitry. For each input signal segment of length
N.sub.seg, the VAD 20 makes a decision whether speech is present (V=1), or
not (V=0). The decision is made by comparing the power level of the signal
in each segment to a given threshold. However, since the noise power is
expected to vary, the threshold must be adapted to the noise level.
Let .lambda..sub.m denote the noise power in the mth segment and Y.sub.m
the input noisy signal power in that segment, i.e.,
##EQU1##
where y.sub.m (n) is the n-th input signal sample in the m-th segment,
which can be written under an additive noise assumption as:
y.sub.m (n)=x.sub.m (n)+v.sub.m (n), Equation 2
where x denotes the clean speech signal and v is the noise.
One could then decide that speech is present in the mth segment if Y.sub.m
>.lambda..sub.m, where .lambda..sub.m is the estimated noise power for
that segment. However, since even if the noise is stationary, a short-term
estimate of its power (when speech is absent) would fluctuate from segment
to segment, one should use a somewhat higher threshold value than
.lambda..sub.m to avoid too frequent false decisions that speech is
present. Hence the noise threshold value, Th.sub..lambda. (m) to which
Y.sub.m is compared is chosen to be
Th.sub..lambda. (m)=b.sub..lambda. .lambda..sub.m,b.sub..lambda.
>1,Equation 3
where b.sub..lambda. is a bias factor to account for this effect. Too
large a bias factor may cause the VAD to decide that speech is absent
(V=0) at low speech levels (e.g., unvoiced speech), so b.sub..lambda. is
typically limited to values below 2. Values in the range of 1.1 to 1.6,
adapted to the noise level, have been used.
Furthermore, since Y.sub.m may also exhibit undesired fluctuations from
segment to segment, particularly when the segments are short, smoothing of
the short term input power is done by the following recursive relation:
Y.sub.m.sup.s =.alpha..sub.Y Y.sub.m-1.sup.s
+(1-.alpha..sub.Y)Y.sub.mEquation 4
where 0<.alpha..sub.y <1 is a smoothing factor, and Y.sub.m.sup.s is the
smoothed short-term input power.
Thus, the VAD decision rule is:
V=1 (speech present) if Y.sub.m.sup.s >Th.sub..lambda. (m)
V=0 (noise only) if Y.sub.m.sup.s .ltoreq.Th.sub..lambda. (m)Equation 5
Since the power of a typical speech utterance decreases slowly at its end
(as compared to the typically fast onset of speech), it is customary in
the art to keep the decision V=1 for a few more segments following the end
of an utterance (a technique known as "hangover"). This avoids clipping
(when V is considered as a gain function) of the tail of the utterance,
which could result from deciding V=0 too soon. When designing a VAD one
should then generally set a value for the hangover interval,
T.sub.hngovr,, which determines the corresponding number of
hangover-segments, L.sub.hngovr, via the relation L.sub.hngovr =.left
brkt-bot.T.sub.hngovr /T.sub.step .right brkt-bot. where T.sub.step is the
duration of the segment update interval.
Since the decision in Equation (5) is based on the smoothed input power
Y.sub.m.sup.s, there is already a natural hangover because of the
smoothing. Hence, T.sub.hngovr is initially limited to less than 0.1 sec.
T.sub.hngovr can also be adapted to the noise level, as known in the art
(see E. Paksoy, K. Srinivasan, and A. Gersho, "Variable Rate Speech Coding
with Phonetic Segmentation," ICASSP-93, Minneapolis, pp. II-155-II-158,
1993, incorporated by reference), for instance by allowing it to vary from
64 msec to 192 msec. It is also common in the art (see ETSI-GSM Technical
Specification: Voice Activity Detector, GSM 06.32 Version 3.0.0, European
Telecommunications Standards Institute, 1991, incorporated by reference)
to avoid a hangover if the condition V=1 prevails only for just a few
segments before deciding V=0, since such a situation is attributed to a
noise burst, too short to be considered a speech utterance. Such a burst
detection mechanism is also preferably implemented in the VAD 20 used in
the invention with the burst-interval T.sub.burst set to a maximum of 64
msec.
As the lower envelope approach of the invention is described, an indication
is needed whether the decision V=1 is due to a hangover condition. A flag
HNG is used to indicate this condition. Thus, HNG=1 when the VAD is in a
hangover state, and HNG=0 when it is not.
A significant issue in nonstationary environments is estimating the noise
power level as it varies from segment to segment. It is typically assumed
in the art that the initial segments contain noise only, and hence they
can be used to obtain an initial estimate of the noise power. Then,
whenever the VAD's decision is that a segment does not contain speech
(V=0), the noise level estimate is updated using recursive smoothing of
the form:
.lambda..sub.m+1 =.alpha..sub..lambda. .lambda..sub.m
+(1-.alpha..sub..lambda.)Y.sub.m if V(m)=0 Equation 6
It is kept unchanged if V(m)=1. cc, is a smoothing factor,
0<.alpha..sub..lambda. <1. V(m) is the value of the VAD decision for the
m-th segment.
In the invention the recursion can be applied directly to the noise
threshold (when speech is absent), namely by:
Th.sub..lambda. (m+1)=.alpha..sub..lambda..sup.Th Th.sub..lambda.
(m)+(1-.alpha..sub..lambda..sup.Th)b.sub..lambda. Y.sub.m.sup.s if
V(m)=0Equation 6
where the smoothing factor 0<.alpha..sub..lambda..sup.Th <1 should be
smaller than .alpha..sub..lambda. of Equation (6), since in Equation (7)
an already smoothed version, Y.sub.m.sup.s, of the input signal power is
used.
This approach for updating the noise level is effective when speech is
absent and the noise level does not increase rapidly. However, even a
relatively small increase in noise power (e.g., by a factor equal to the
bias factor b.sub..lambda.) during a speech utterance will cause the VAD
20 to miss the end of the utterance. VAD 20 will then continue to assume
that speech is present until the noise level descends below b.sub..lambda.
times the value it had before that utterance began. A decrease in noise
level, even when speech is present, poses no significant problem since the
VAD 20 can still detect the end of the utterance properly and the noise
threshold will eventually decay to the lower noise level, through the
application of Equation (7).
When a transition of the form of a relatively steep increase in noise level
occurs, the noise threshold tracking of Equation (7) may fail, even if
speech is absent. In this case the VAD 20 will interpret the change in
level as an onset of speech (unless additional attributes of the signal
are examined, like presence of pitch, rate of zero crossings, etc. as done
in some more complex VADs known in the art, such as those reflected in:
ETSI-GSM Technical Specification: Voice Activity Detector, GSM 06.32
Version 3.0.0, European Telecommunications Standards Institute, 1991;
ITU-T, Annex A to Recommendation G.723.1: Silence Compression Scheme for
Dual Rate Speech Coder for Multimedia Communications Transmitting at 5.3 &
6.3 Kbit/s, May 1996; ITU-T, G.729A: A Proposal for a Silence Compression
Scheme Optimized for the ITU-T G.729 Annex A Speech Coding Algorithm, by
France Telecom/CNET, June 1996; R. Tucker, "Voice Activity Detection using
a Periodicity Measure", IEE Proceedings-I, Vol. 139, No. 4, pp. 377-380,
August 1992, each incorporated by reference). Such a transition in noise
level is typical in mobile communication environments (e.g., a passing
truck, car acceleration, opening a window, turning on the air conditioner,
etc.).
B. Power Stationarity Test
One way to alleviate the effect of such a transition on the VAD 20
(assuming that following the transition the noise level becomes stationary
for a while) is to measure the short term power stationarity of the input
over a long enough interval T.sub.PS (say, 1 sec). Since speech is not
expected to be stationary over such a relatively long interval, that
measurement can indicate the absence of speech. Thus, following the
transition to a higher noise level, if the measured power within that test
interval does not change much (say, by less than 2 or 3 dB), the input
signal can be assumed to be noise only. The noise threshold can then be
updated, followed by tracking according to Equation (7).
Before this approach is described, it should be noted that the examples
presented are for a segment length of N.sub.seg =256 samples at a sampling
rate of f.sub.s =8 KHz (i.e., a segment duration T.sub.seg =N.sub.seg
/f.sub.s =32 msec), and an update step, N.sub.step =T.sub.step f.sub.s
=N.sub.seg (i.e., no overlap between consecutive segments).
FIG. 2 demonstrates the use of this approach for a transition due to a
steep increase of helicopter noise. In this figure the thin solid line
describes the smoothed input power level, Y.sub.m.sup.s, (on a logarithmic
scale) as it changes from segment to segment. The dotted line in this
figure denotes the noise threshold, Th.sub..lambda., and the superimposed
rectangular pulse defines the interval for which the VAD 20 makes the
decision that speech is present (i.e., V=1, which is a wrong decision in
this case). It is seen from the figure that the transition ends at about
segment 110 and only about 32 segments after the transition has ended (the
test interval, T.sub.PS, is 1 sec long), at segment 142, the noise
threshold is finally updated. Following this update the VAD 20 produces
the correct decision V=0. The corresponding waveform is shown in FIG. 3,
with decisions of VAD 20 superimposed.
Clearly this approach involves a delay of the duration of the noise
transition from one level to another plus the duration of the power
stationarity test interval (a total of about 100 segments (approx. 3 sec),
in the example shown in FIG. 2).
The short term power stationarity test is implemented in the VAD 20 by
first loading the values of Y.sub.m.sup.w in a cyclic buffer (B.sub.Y) 30
of length L.sub.PS =.left brkt-bot.T.sub.PS /T.sub.step .right brkt-bot.
(an integer equal to the number of short term power measurements done in
the test interval). Then, for each segment, the ratio between the largest
and smallest data values present in buffer 30 are compared to a given
threshold Th.sub.PS. If this ratio is less than or equal to Th.sub.PS, the
power stationarity test is satisfied (PST=1); otherwise PST=0. In the
example shown in FIGS. 2 and 3, T.sub.SP =1 sec. (L.sub.PS =31) and
Th.sub.PS =1.6 (2 dB). Formally, the equations which describe the power
stationarity test (PS test) are as follows:
##EQU2##
The noise threshold is updated when the test result switches from PST=0 to
PST=1 and speech is assumed present (V(m-1)=1), i.e.,
if {PST(m-1)=0 & PST(m)=1 & V(m-1)=1}, set Th.sub..lambda.
(m)=b.sub..lambda. Y.sub.m.sup.s Equation 10
To avoid numerical problems the minimum value allowed in the buffer 30 is 1
(according to Equation (8)). The maximum possible value in the buffer 30
is given by
Y.sub.max =2.sup.2(N.sbsp.B.sup.-1), Equation 11
where N.sub.B is the number of bits in the input signal representation (16
bits in simulations by the Inventor). The buffer 30 must be initialized
with 1's. It is also preferable to reset the buffer 30 every time the VAD
20 switches its decision.
It may be noted that the power stationarity test is actually a simplified
form of a more elaborate test based on measuring spectral changes between
consecutive segments, which is a central part of the more complex prior
art VADs mentioned above. There is therefore a tradeoff between complexity
and delay.
The power stationarity test known in the art and described above still does
not solve the problem of tracking noise level increases which occur during
and between closely spaced speech utterances, unless there are relatively
long gaps between utterances (longer than the test interval) and the noise
level is stationary within those gaps.
As noted, these and other problems are addressed in the system and method
of the invention, including by using a lower envelope method for updating
the noise threshold. This approach can also help in updating the noise
threshold following a steep transition, but may involve a longer delay
than the short term power stationarity test described above. On the other
hand it does not require that the noise power becomes stationary following
the transition.
C. System and Method of the Invention Including Using Lower Envelope for
Updating Noise Threshold
As explained above, one significant problem addressed by the invention is
that of how to update the noise threshold when the input noise level
increases during and between closely spaced speech utterances. In such a
situation, if the noise threshold, Th.sub..lambda., is not properly
updated, the VAD 20 will continue to decide that speech is present,
although it is not, until the power stationarity test is satisfied.
The noise threshold approach of the invention is based in part on the
observation that the power level of the input signal decreases even during
short gaps in the speech signal (e.g., between words and particularly
between sentences) to the level of the noise. Hence, if the lower envelope
of the signal power is properly tracked, the noise threshold can be
properly updated to the new level at the end of an utterance. Advantage is
taken of the fact that for the purpose of detecting speech absence, a
proper update of the noise threshold only needs to be done at the end of
an utterance and not necessarily while speech is present. This may not be
the case in speech enhancement systems where the knowledge of the noise
level (and its spectral shape) in every segment during the speech
utterance is important, as it directly affects the noise attenuation
applied in each segment. Since this is a rather difficult task, and
typically the noise does not vary that much during an utterance (except
for transitions), updating the noise in the gaps between utterances is
usually satisfactory and is commonly done. The VAD 20 however should
properly detect the end of utterances, which is one problem addressed by
the invention.
An illustration of the basic lower envelope approach used in the invention
is shown in FIG. 4. This figure reflects two sentences in white noise
whose power increases in time at the rate of about 1 dB/sec. The initial
SNR value is about 15 dB. As in FIG. 2, the thin solid line is the
smoothed input signal power, Y.sub.m.sup.s, the dotted line is the noise
threshold (Th.sub..lambda.) 50 used by the VAD 20 according to Equation
(5). The dashed line is the lower envelope 40, a signal which is used to
indicate the instants at which the value of Th.sub..lambda. should be
updated. In the illustrative time domain VAD 20 the value of the lower
envelope 40 at an update instant is used as the value to which the noise
threshold 50 is updated to, but this need not be the case in VADs which
use the spectral shape of the noise.
The approach is that an update of the noise threshold 50 is performed only
at those segments for which the VAD's last decision was V=1 (speech
present) and the lower envelope 40 is at an inflection point 60, that is,
turning up (following a segment at which the envelope was nonincreasing).
The inflection point 60 is chosen because it potentially indicates that
the lower envelope 40 has reached the noise level, as for instance
illustrated in FIG. 4 towards the end of the second utterance (around
segment 175). Updating the noise threshold 50 at inflection point 60 of
the lower envelope 40 before the end of the utterance does not necessarily
reflect the actual noise level within the utterance. It does however help
in reaching the proper noise threshold value at the end of the utterance,
or shortly after it.
Clearly, as shown in FIG. 4 the VAD 20 decides that speech is present (V=1)
at all those segments where the input power level is above the dotted
line. This is indicated by the superimposed rectangular pulses. In
addition, the value V=1 is kept for 3 more segments (corresponding to
T.sub.hngovr 96 msec) beyond the crossover point between the input power
and the noise threshold 50 at the end of the utterance, due to the
hangover condition discussed above. Decisions of VAD 20 for this example
are shown superimposed on the input waveform in FIG. 5. It is seen that
the VAD 20 performs adequately, in spite of the increase in noise level,
by well beyond the factor b.sub..lambda. =1.3 (.about.1.2 dB) while speech
is present.
The value of lower envelope 40 at the mth segment, L.sub.E (m), is
generated according to the following expression:
##EQU3##
where r.sub.E >1 is the lower envelope rate-factor.
The value of lower envelope 40, L.sub.E (m), is used here to conditionally
update the noise threshold according to:
If {V(m-1)=1 & HNG(m-1)=0} & {L.sub.E (m)>L.sub.E (m-1) & L.sub.E
(m-1).ltoreq.L.sub.E (m-2)}, set Th.sub..lambda. (m)=L.sub.E (m).Equation
13
Otherwise, the earlier value of Th.sub..lambda. is kept.
Again, HNG is the hangover flag. The condition in Equation (13) states that
an update is performed if the lower envelope 40 is at an inflection point
60, provided that the last decision of VAD 20 is that speech is present
(V=1, but not in a `hangover` state). The decision of VAD 20 for the
current segment (m) is then performed according to Equation (5), except
that if the conditional update, according to Equation (13), is performed
at segment m, V(m) is set to 1.
A significant issue in the implementation of the invention is the selection
of the lower envelope rate factor r.sub.E (Equation (12)). On one hand,
r.sub.E should be less than the rate of increase of the speech signal at
the onset of each part of the utterance when the noise is stationary. This
later rate is typically lower towards the end of an utterance than at its
onset. In addition, it gets lower as the noise level in which the signal
is immersed gets higher. Hence, to accommodate these requirements,
adaptation in setting the value of r.sub.E is desirable, and is described
below.
D. Supplementing Invention with Power Stationarity Test
As mentioned above, the lower envelope approach implemented in the
invention can be effective in updating the noise threshold 50 after the
occurrence of a steep increase in the noise level due to a transition like
the one shown in FIG. 2. However, this processing may involve a longer
delay than the conventional power stationarity test. The reason is that
the rate of increase (slope) of the lower envelope 40 is limited to match,
on average, the expected increase of a speech signal. Since the VAD 20
assumes during a steep transition that speech is present, the lower
envelope 40 will satisfy the conditions for an update (according to
Equation (13)) only after a relatively long delay. Hence, it would be of
advantage to apply this supplemental test to the invention, at least under
certain circumstances. This can be done by first applying the power
stationarity test in each segment, and whenever it results in an update of
the noise threshold 50 (according to Equation (10)), forcing the lower
envelope 40 to the value of the input power. That is, what needs to be
added to Equation (10) is:
set L.sub.E (m)=Y.sub.m.sup.s if the condition in Equation (10)
holds.Equation 14
Equation (14) precedes therefore the operations performed according to
Equation (12) and (13), which are then followed by the operation of
Equation (5). A schematic flow chart of that sequence is shown in FIG. 7.
The combination of these approaches is shown in FIG. 6, which adds the
lower envelope (dashed line) 40 to FIG. 2, and the effect of Equation
(14). This figure also indicates that without the power stationarity test,
the update of the noise threshold 40 would have happened later, since the
slope of the lower envelope 40 is relatively low compared to the rate of
increase of the transition. Furthermore, forcing the lower envelope 40 to
be updated to the value of the input power after the transition ensures
that VAD 20 will function as intended once a speech utterance appears.
Otherwise, if a speech utterance appears before the lower envelope 40
reaches the input noise level, VAD 20 may not reach that level in time,
even at the end of the utterance. Thus, the VAD 20 may not detect the end
of the utterance if during the utterance there was even a small increase
(beyond the factor b.sub..lambda.) in noise level.
In addition, even if the power stationarity test happens to fail, e.g.,
because the fluctuations in noise power level following the transition are
too large, the lower envelope 40 would at least eventually catch up, and
the VAD 20 will recover and resume proper functioning. Otherwise this
would happen only if the noise level decreases to about the level before
the transition.
E. Parameter Selection and Adaptation
The implementation of the invention involves the selection of various
parameters, and for some of them, like the lower envelope rate factor,
r.sub.E, also adaptation.
Before discussion of selection of the parameters, the issues of segment
length and segment update-step are examined. The selection of these values
is usually dictated by a given application. Yet, because a typical speech
"quasi-stationarity" interval is limited to about 32 msec, the selection
above of a segment length of duration T.sub.seg =32 msec (corresponding to
N.sub.seg =256 samples at a sampling rate of fs=8 KHz) is taken as the
nominal segment length, T.sub.seg. Usually the segment update step
N.sub.step is selected to be equal to the segment length N.sub.seg. Yet,
there is no reason to restrict a user to this choice. Hence, other segment
length and update step values that may be used via the
segment-length-ratio, r.sub.seg, and update-step-ratio, r.sub.step, which
are defined as follows:
##EQU4##
Consideration is now given to the parameter, r.sub.E the lower envelope
rate-factor in Equation (12). According to the discussion above, one
requirement for r.sub.E is that during the presence of speech its value
should be within a limited range r.sub.E.sup.min .ltoreq.r.sub.E
.ltoreq.r.sub.E.sup.max. The lower value, r.sub.E.sup.min >1, should be
selected to provide proper operation of the VAD 20 when the noise is
stationary. The upper value, r.sub.E.sup.max >r.sub.E.sup.min, should be
selected to provide the largest slope possible when the noise increases
during a speech utterance. However, r.sub.E.sup.max should not be too
large compared to the rate of increase in the short term speech power at
the low power end of the utterance. Based on simulations, the inventor has
chosen the lower envelope slopes (on a logarithmic scale) to be in the
range of about 1.3 dB/sec to 13 dB/sec, which for N.sub.seg =N.sub.step
=256 and fs=8 KHz correspond to 1.01.ltoreq.r.sub.E .ltoreq.1.1. To
accommodate different segment lengths and segment update-step values, the
calculation is:
r.sub.E.sup.min =1+0.01r.sub.seg r.sub.step ; r.sub.E.sup.max
=1+0.1r.sub.seg r.sub.step (Speech present) Equation 14
The actual value of r.sub.E used during speech presence is set in the above
range at the onset of the utterance (i.e., when V(m)=1 & V(m-1)=0)
according to two other considerations. Those considerations are the rate
of change of the noise power level and the noise power level itself. The
rate of change in noise power level is monitored by computing at each
onset of a speech utterance the ratio between the noise power value
measured just before the onset and the value obtained just before the
onset of the previous utterance. This ratio is denoted by r.sub..lambda.,
and N.sub.V represents the number of segment updates between the two
measurements. These two parameters and the lowest value allowed for
r.sub.E, denoted above by r.sub.E.sup.min, are then used to determine a
rate-factor value denoted by r.sub.E.sup.l, via
r.sub.E.sup.I =max(r.sub.E.sup.min,(r.sub..lambda.).sup.1/N.sbsp.v)Equation
17
A limit is set on the value of r.sub.E which depends on the estimated value
of the noise power, .lambda., just before the onset of the utterance, as
compared to the maximal possible input power level in the system,
Y.sub.max, as given by Equation (11).
Since just before the utterance onset, .lambda.=Th.sub..lambda.
/b.sub..lambda. (see Equation (3)), and b.sub..lambda. is close to 1,
Th.sub..lambda. is preferably used in the following definition of the
Logarithmic Noise to Peak-Signal Ratio (LNPSR):
P.sub.N =log(Th.sub..lambda.)/log(Y.sub.max),0.ltoreq.P.sub.N
.ltoreq.1,(V=0) Equation 18
P.sub.N is then used to obtain another rate-factor value, denoted by
r.sub.E.sup.II,
r.sub.E.sup.II =r.sub.E.sup.min +(r.sub.E.sup.max
-r.sub.E.sup.min)(1-P.sub.N) Equation 19
Finally, the current value chosen for r.sub.E which is to be used through
the current speech utterance is given by:
r.sub.E =min(r.sub.E.sup.I, r.sub.E.sup.II) (Speech Present)Equation 20
This value r.sub.E is in the desired range r.sub.E.sup.min .ltoreq.r.sub.E
.ltoreq.r.sub.E.sup.max, and also takes into account both the expected
increase in noise level and the noise level itself, under the above range
constraints.
As noted above, the value of r.sub.E according to Equation (20) is used
during the presence of the current speech utterance. Once VAD 20 has
detected the end of the utterance, the value r.sub.E can be set according
to the actual rate of increase of the noise power, i.e., to
r.sub.E =r.sub.E.sup.I (Speech absent) Equation 21
Other parameters used in the implementation of the invention are: The
hangover-interval, T.sub.hngovr, from which L.sub.hngovr is computed; the
smoothing factors .alpha..sub.Y and .alpha..sub..lambda..sup.Th, appearing
in Equation (4) and (7), respectively; the noise bias-factor,
b.sub..lambda., appearing in Equation (7); and the power stationarity
test-interval, T.sub.PS (from which L.sub.PS is determined), and the
threshold Th.sub.PS appearing in the power stationarity test of Equation
(9). As mentioned above, a typical value for T.sub.PS is 1 sec. The other
parameters could also be set to fixed values. Yet, the inventor has found
(and for the hangover-interval it is suggested in E. Paksoy, K.
Srinivasan, and A. Gersho, "Variable Rate Speech Coding with Phonetic
Segmentation," ICASSP-93, Minneapolis, pp. II-155-II-158, 1993) that there
is an advantage in adapting these parameters to the noise-power level.
This is done using the LNPSR, P.sub.N, defined in Equation (18), according
to:
.alpha..sub.Y =.alpha..sub..lambda..sup.Th =1-[.delta..sub.0 +.delta..sub.1
(1-P.sub.N)]r.sub.seg r.sub.step Equation 22
where, based on simulations, selection is made of .delta..sub.0
=.delta..sub.1 =0.2.
The motivation for this adaptation is that as the noise level increases it
is of advantage to have more smoothing, which is achieved by making the
smoothing factor closer to 1. For the nominal values of r.sub.seg
=r.sub.step =1, and since P.sub.N is between 0 (no noise) and 1, the
values of the smoothing factors are in the range of 0.6 to 0.8. If a fixed
value is desired, the preferred value is 0.7.
The adaptation of the hangover interval is done according to:
L.sub.hngovr =[L.sub.hgovr.sup.min (1+2P.sub.N)], Equation 23
where L.sub.hngovr.sup.min is the minimum number of hangover segments (very
low noise case), obtained from the minimum hangover-interval
L.sub.hngovr.sup.min via L.sub.hngovr.sup.min =.left
brkt-bot.T.sub.hngovr.sup.min /T.sub.step .right brkt-bot.. The inventor
has used T.sub.hngovr.sup.min =64 msec. With T.sub.step =32 msec,
L.sub.hngovr can vary from 2 to 6, depending on the noise level, via
P.sub.N.
As for the remaining two parameters, in practice values have been used
according to:
b.sub..lambda. =1.6-0.5P.sub.N .fwdarw.1.1<b.sub..lambda. .ltoreq.1.6
Th.sub.PS =2-P.sub.N .fwdarw.1<Th.sub.PS .ltoreq.2 Equation 24
The need for adapting these two parameters comes from the fact that as the
noise level increases, the margin of speech power level above the noise
decreases. Hence, to avoid `speech clipping` (i.e., deciding V=0) of
low-power speech segments, b.sub..lambda. should be reduced. As for
Th.sub.PS, it should be reduced then as well since otherwise low level
speech power (above the noise) could meet the power stationarity test and
cause an undesired update of the noise threshold 50.
The above adaptation is performed only when speech is absent (V=0), because
only then is the value of P.sub.N updated (see Equation (18)).
With the above setting of parameters the inventor has obtained good
performance down to about 0 dB SNR, as demonstrated below.
F. Algorithm Summary and Simulation Results
Before presenting simulation results, the main processing steps in the
execution of the invention is presented, in conjunction with FIG. 7.
1. Initialization:
(i) Given the sampling frequency f.sub.s and the number of bits, N.sub.B,
in the input signal representation, set or compute (the relevant equation
numbers appear in parenthesis; the arrow, .fwdarw., denotes "from which,
compute") the following parameters:
T.sub.seg (.fwdarw.N.sub.seg,r | | |
