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Claims  |
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I claim:
1. A frequency-domain adaptive filter comprising:
a first input signal line to receive an input signal;
a second input signal line to receive a send signal;
a plurality of memory stores, each said memory store having a
frequency-domain coefficient vector;
a plurality of weights, each said weight corresponding to one of said
memory stores;
transformation means coupled to said first input signal line for producing
a plurality of frequency-domain input blocks from said input signal;
response means for producing a time-domain response signal from said
coefficient vectors and said frequency-domain input blocks, including
first means coupled to said plurality of memory stores for multiplying the
frequency-domain coefficient vector in each said memory store with the
weight corresponding to said memory store to produce weighted coefficient
vectors, second means for multiplying said weighted coefficient vectors
with said frequency-domain input blocks to obtain product terms, means for
summing together said product terms, and means for transforming said
summed product terms to the time domain, thereby producing said
time-domain response signal;
combining means for producing a filtered send signal from said time-domain
response signal and said send signal;
an outgoing signal line coupled to said combining means to transmit said
filtered send signal; and
means for adapting said plurality of coefficient vectors, including:
error means for producing a frequency-domain error vector from said
filtered send signal;
correlation means for producing correlation terms from said
frequency-domain error vector and said frequency-domain input blocks;
summing means coupled to said plurality of memory stores for adding said
correlation terms to said coefficient vectors in said memory stores; and
constraint means coupled to said plurality of memory stores for selecting
at least one of said memory stores and for constraining the coefficient
vector stored in said selected one of said memory store.
2. The frequency-domain adaptive filter of claim 1, wherein each said
weight is a vector of numerical factors.
3. The frequency-domain adaptive filter of claim 1, wherein said weight
corresponding to a first of said plurality of memory stores has a maximum
weight value and said weights corresponding to subsequent ones of said
memory stores have decreasing weight values.
4. The frequency-domain adaptive filter of claim 3, wherein said weight
values are based upon a monotonically decaying function.
5. The frequency-domain adaptive filter of claim 1 further including a
speakerphone having a speaker coupled to said first input signal line and
a microphone coupled to said second input signal line; and wherein said
input receive signal is a telephone signal that is broadcast by said
speaker into a room, and said send signal is an output of said microphone.
6. A method of filtering an outgoing signal by adaptively estimating the
linear transfer function of a mainly acoustic system between an incoming
reference signal and said outgoing signal, said outgoing signal including
echo signals produced by said mainly acoustic system in response to said
reference signal, said method of filtering comprising the steps of:
(a) providing an array of coefficient vectors to produce a frequency domain
model of said linear transfer function, said array of coefficient vectors
representing an initial estimate of said linear transfer function;
(b) selecting a plurality of weights, each said weight corresponding to an
entry in said array of coefficient vectors; and
(c) iterating the following substeps of:
(i) receiving said reference signal from a first input source;
(ii) transforming said reference signal to produce frequency-domain input
blocks;
(iii) computing a time-domain response from said frequency-domain input
blocks and said array of coefficient vectors, including multiplying said
array of coefficient vectors with said corresponding plurality of weights
to produce weighted coefficient vectors, multiplying said weighted
coefficient vectors with said frequency-domain input blocks to produce
product terms, summing said product terms to produce a sum term, and
transforming said sum term to produce said time-domain response, said
time-domain response representing an estimated signal produced by said
mainly acoustic system in response to said reference signal;
(iv) receiving said outgoing signal from a second input source;
(v) subtracting said time-domain response from said outgoing signal thereby
filtering said outgoing signal, whereby said echo signals are
substantially canceled thereform;
(vi) transmitting said filtered outgoing signal; and
(vii) adapting said array of coefficient vectors, said substep of adapting
including the substeps of:
(A) generating a frequency-domain error vector from said filtered outgoing
signal;
(B) correlating said frequency-domain error vector with said
frequency-domain input blocks to produce correlation terms;
(C) adding said correlation terms to said array of coefficient vectors; and
(D) following said substep of adding, selecting at least one entry of said
added array of coefficient vectors and constraining the coefficient vector
contained in said selected entry, including basing said substep of
selecting on said plurality of weights such that continued iterations of
said substep of selecting results in each vector of said added array of
coefficient vectors being selected at a frequency proportional to the
weight corresponding to each said vector,
whereby said added array of coefficient vectors more closely estimates said
linear transfer function than a previous iteration.
7. The method of claim 6 wherein each of said weights is a vector of
numerical factors and said selecting said plurality of weights includes
selecting numerical factors of less than one.
8. The method of claim 6 wherein said selecting said plurality of weights
includes selecting an exponentially decaying function and computing a
value for each said weight based upon said exponentially decaying
function, thereby approximating the reverberation decay of said acoustic
system.
9. The method of claim 6, wherein each entry of said array of coefficient
vectors has a unique identification number; and the method further
includes selecting a list containing a sequence of said unique
identification numbers, wherein said substep of selecting at least one
entry includes indexing into said list to obtain one of said unique
identification numbers and selecting the corresponding entry.
10. The method of claim 9, wherein said step of iterating includes
incrementing a counter with each iteration, and said step of indexing
includes computing an N modulo value of said counter, where N is the size
of said list containing said unique identification numbers.
11. A frequency-domain adaptive filter for canceling acoustic echo in a
transmit signal, said acoustic echo produced by an acoustic system in
response to an input signal, said filter comprising:
means for receiving said input signal;
means for receiving said transmit signal;
transformation means for generating a plurality of frequency-domain input
vectors from said input signal;
a plurality of frequency-domain coefficient vectors, each of said
coefficient vectors approximating said acoustic echo at uniformly
increasing delays in time;
a plurality of weights having a one-to-one correspondence with said
coefficient vectors;
multiplication means for multiplying each of said coefficient vectors with
said corresponding weight to produce a plurality of weighted coefficient
vectors;
means for generating a time-domain estimated echo signal from said
frequency-domain input vectors and said weighted coefficient vectors;
means for subtracting said time-domain estimated echo signal from said
transmit signal to produce a filtered signal;
output means for transmitting said filtered signal; and
adaptation means for updating said frequency-domain coefficient vectors,
including:
error means for generating a frequency-domain error vector from said
filtered signal;
means for generating correlation terms from said frequency-domain error
vector and said frequency-domain input vectors;
means for adding said correlation terms to said frequency-domain
coefficient vectors, thereby adjusting said coefficient vectors;
scheduling means for selecting at least one of said adjusted coefficient
vectors, said scheduling means preferentially selecting adjusted
coefficient vectors which correspond to shorter delays in time relative to
others of said adjusted coefficient vectors; and
constraint means for constraining said selected adjusted coefficient
vectors.
12. The frequency-domain adaptive filter of claim 11 wherein each of said
weights is a vector of numerical factors, each said numerical factor being
less than one.
13. The frequency-domain adaptive filter of claim 11 wherein each of said
weights represents a numerical factor, each of said numerical factors
being based upon an exponentially decaying function, thereby emulating the
acoustic response envelope of said acoustic system.
14. A frequency-domain adaptive filter comprising:
a first input signal line to receive an input signal;
a second input signal line to receive a send signal;
a plurality of memory stores, each said memory store having a
frequency-domain coefficient vector;
transformation means coupled to said first input signal line for producing
a plurality of frequency-domain input blocks from said input signal;
response means for producing a time-domain response signal from said
coefficient vectors and said frequency-domain input blocks;
combining means for producing a filtered send signal from said time-domain
response signal and said send signal;
an outgoing signal line to transmit said filtered send signal; and
means for adapting said plurality of coefficient vectors, including:
error means for producing a frequency-domain error vector from said
filtered send signal;
correlation means for producing correlation terms from said
frequency-domain error vector and said frequency-domain input blocks, said
frequency-domain error vector having a corresponding step size vector and
a corresponding weighting vector, said correlation means including means
for producing a plurality of complex conjugate values of said
frequency-domain input blocks means for multiplying said step size vector,
said weighting vector and said complex conjugate values to produce said
correlation terms;
summing means coupled to said plurality of memory stores for adding said
correlation terms to said coefficient vectors in said memory stores; and
constraint means coupled to said plurality of memory stores for selecting
at least one of said memory stores and for constraining the coefficient
vector stored in said selected one of said memory store.
15. The frequency-domain adaptive filter of claim 14 further including a
speakerphone having a speaker coupled to said first input signal line and
a microphone coupled to said second input signal line, wherein said input
receive signal is a telephone signal that is broadcast by said speaker
into a room, and said send signal is an output of said microphone.
16. The frequency-domain adaptive filter of claim 14 wherein said weighting
vector includes weights which are based upon a monotonically decaying
function. |
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Claims |
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Description |
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TECHNICAL FIELD
The invention relates to frequency-domain adaptive filters, and in
particular to a device and a method for a block frequency-domain adaptive
filter as applied to acoustic echo cancellation in a full-duplex
speakerphone device.
BACKGROUND ART
To date, most speakerphones operate in a half-duplex mode; i.e. only one
caller can be heard at a given instant. A half-duplex arrangement imparts
an annoying chopping of speech as the near-end caller and the far-end
caller attempt to speak at the same time. A full-duplex speakerphone, on
the other hand, allows for the near-end and far-end callers to talk
simultaneously, thus avoiding the distracting interruption of speech.
Full-duplex speakerphones, however, suffer a problem due to the
regenerative effects of acoustic echo paths that occur between the speaker
and the microphone. The problem manifests itself as audible echo and
possibly a howlback condition as the echo is retransmitted and
re-amplified between the near-end and far-end speakerphones. An acoustic
echo canceller (AEC) is commonly employed to eliminate these problems.
In general, an AEC is an adaptive filter which models the acoustic response
of a room. A modeling component generates an estimate of the echo signal
that will be formed by the room using an incoming signal from a far-end
caller. The filter operates on an outgoing signal which includes a speech
signal of the near-end caller and echo signals resulting from acoustic
reflections of the incoming signal within the room. A "clean" signal,
formed by subtracting the estimated echo signal from the outgoing signal,
is then transmitted to the far-end caller. By comparing the "clean" signal
to the incoming signal, an adaptation component of the AEC adapts the
filter to more accurately approximate the room response.
An AEC performs a computationally demanding task. To model the room
response, it is typical to have a 2000 tap filter capable of computing the
next sample at a rate of 8000 Hz for a normal telephone channel. The AEC
is generally implemented on some type of digital processor such as a
microprocessor, a digital signal processor (DSP), a microcontroller or an
application specific digital integrated circuit (digital ASIC).
The room response modeled by AECs is generally a linear model, consisting
of a series of coefficients which represent the strength of the acoustic
signal for a period of time. Pragmatic AECs have used the finite impulse
response (FIR) filter as the model. The coefficients of the FIR filter are
usually adapted by a least mean squares (LMS) technique to match the room
response. This is referred to as the time-domain LMS technique.
Time-domain LMS has the advantage of operating without imposing any
significant delay between accepting the outgoing signal from the near-end
speakerphone, which contains the desired near-end speech and the undesired
room echo, and generating the "clean" signal for subsequent transmission
to the far-end speakerphone. However, this quick response time is obtained
at the expense of a computationally demanding process. Moreover, the rate
of convergence, i.e. the time it takes for a filter to adapt its
parameters to adequately model the acoustic characteristics of the room,
using the time-domain LMS approach is very slow because voice signals are
so highly correlated.
The most popular alternative to the time-domain LMS technique which
exhibits improved convergence performance is a method known as subband
filtering. Subband filtering divides the input signal into separate
frequency bands for subsequent processing. This divide-and-conquer
approach converges faster than the standard time-domain LMS method because
there is less correlation between samples in each subband. However, the
tradeoff is an increase in delay due to the necessary additional
processing of a polyphase filter at the front end of the subband filter to
compute the initial subband filter banks.
It has been well known that dramatic computational savings can be realized
by performing the computations of the FIR filter in the frequency-domain
instead of operating in the time-domain. See generally Clark et al., "A
Unified Approach to Time--and Frequency-Domain Realization of FIR Adaptive
Digital Filters," Vol. ASSP-13, No. 5, IEEE Transactions on Acoustics,
Speech, and Signal Processing, pages 1073-1083 (October 1983) and U.S.
Pat. No. 4,807,173 to Sommen et al. Frequency-domain filtering employs the
same basic approach as described above, except that the signals are
processed in the frequency-domain. Thus, a time-domain incoming (input)
signal is sampled and converted to the frequency-domain, using for example
a particular implementation of the Discrete Fourier Transform (DFT) known
as the fast Fourier transform (FFT). A frequency-domain model of the room
response is used to generate a frequency-domain estimate of the expected
echo, which is converted to the time-domain and subtracted from a
time-domain representation of the outgoing signal. The subtracted signal
is 1) sent to the far-end caller and 2) is used by the AEC as an error
signal to adapt the frequency-domain model of the room response. These
frequency-domain quantities, called DFT vectors, are complex vectors whose
elements correspond to a frequency. The individual elements of each vector
are commonly called bins. The basic approach just described suffers from
long delays needed to acquire a sufficiently large sample of the input
signal to compute the necessary FFT. These delays would result in
noticeable periods of silence which would tend to be very distracting to
the human listener.
To minimize delay while achieving efficiency, a better implementation of a
frequency-domain adaptive filter is to have a multiplicity of smaller
blocks of DFT vectors, and to perform the filtering operation using these
smaller blocks. The processing steps are essentially the same as in the
non-blocked approach. However, the room response in this block
frequency-domain adaptive filter, is modeled using an array consisting of
smaller frequency-domain coefficient vectors to provide an estimate of the
echo response. Each of the smaller vectors approximates a portion of the
echo response because each vector represents a smaller period of time.
Furthermore, each vector approximates a portion of the echo response for a
different window of time such that the complete echo response is a
composite of the partial echo responses. See generally, Asharif, M. R. et
al., "Frequency Bin Adaptive Filtering (FBAF) Algorithm and Its
Application to Acoustic Echo Cancelling,"I.E.I.C.E. Transactions, Vol. E
74, No. 8, August 1991, pp. 2276-2282 and Soo, J. et al., "Multidelay
Block Frequency Domain Adaptive Filter," IEEE Transactions on Acoustics,
Speech, and Signal Processing, Vol. 38, No. 2, February 1990, pp.
373-376.
Adaptation of the block frequency-domain coefficient vectors involves a
correlation between the error signal and the input signal. The
frequency-domain coefficient vectors are adjusted by addition of the
resulting correlation vectors, so that the filter characteristics will
move in the direction to minimize residual echo in the error signal.
To date, frequency domain adaptation has proceeded in one of two ways:
constrained and unconstrained. A constrained adaptation involves
additional processing of the N frequency-domain correlation term. In the
constraint operation, the frequency-domain correlation terms are
transformed into N corresponding time-domain terms. The last N/2
time-domain terms are set to zero to eliminate the circular component.
These constrained time-domain terms are then transformed back to the
frequency-domain.
From a theoretical point of view, it is preferred that the frequency-domain
coefficient vectors are adapted in a constrained manner. The reason is
that unconstrained adaptation results in a build-up of a "circular
convolution" component in the frequency-domain coefficients which causes
corruption of the coefficients. By constraining the time-domain
correlation terms, a linear convolution results, thus avoiding the
occurrence of circular convolution altogether. Constrained adaptation,
however, involves two additional DFTs per coefficient vector, and
therefore imposes additional computational burdens on the AEC. On the
other hand, the unconstrained technique converges at a slower rate and, in
the steady state, converges to less accurate coefficient vectors,
resulting in a less accurate model of the room response. Moreover, by
using the unconstrained approach, the circular convolution component may
be large enough to produce distracting audible artifacts.
A further consideration is the fact the echoes resulting from reflections
off the walls, the furniture and other objects in the room exhibit a
decaying response. While the echo signal decays over time, the strength of
the noise component remains substantially undiminished. Left
uncompensated, this masking effect will tend to destabilize the filter,
thus slowing the rate of convergence and the long-term accuracy of the
filter.
An approximation to a constrained adaptation approach is described in U.S.
Pat. No. 4,807,173 to Sommen et al., which relies on the fact that
multiplication of the time-domain window function is equivalent to a
convolution of the window function in the frequency-domain. Sommen et al.
define a specialized time-domain window function to approximate the effect
of a DFT-based constraint operation such that the frequency-domain
convolution operation reduces to three multiplication operations. Sommen
et al. do not disclose a method which addresses the masking effect of the
additive noise due to the presence of a decaying echo response.
An unconstrained adaptive filter is described in U.S. Pat. No. 5,117,418 to
Chaffee et al. However, Chaffee et al. discuss the analogous situation of
cancellation of echoes originating from the imperfections found in the
equipment located at the local telephone switching office. The technique
is commonly known as line echo cancellation (LEC).
An unconstrained adaptive filter is advocated in an article by J. M. P.
Borrallo et al., "On the Implementation of the Partitioned Block Frequency
Domain Adaptive Filter (PBFDAF) for Long Acoustic Echo Cancellation," Vol
27, No. 3, Signal Processing, pages 301-315 (June 1992). Borrallo et al.
teach that the unconstrained approach is computationally efficient, and
that under some favorable conditions, the approach converges to the Wiener
solution. However, given that speakerphones are used in a wide variety of
operating environments, it cannot be assumed that the favorable conditions
anticipated by Borrallo et al. will be present in any particular
situation. The paper also addresses the destabilizing effect of the
additive noise masking the echo signal as it decays over time, and
describes a progressive attenuation method applied during the
cross-correlation step as a way of ensuring filter stability.
It is an object of the present invention to provide an efficient system and
method of echo cancellation for use in a full-duplex speakerphone, which
exhibits a fast convergence and is less sensitive to noise.
It is yet another object of the present invention to provide a system and
method of echo cancellation which can be performed with minimal
computational overhead.
It is therefore an object of the present invention to provide an efficient
system and method of echo cancellation for use in a full-duplex
speakerphone, which exhibits a fast convergence and is less sensitive to
noise.
SUMMARY OF THE INVENTION
The above objects have been met by a frequency-domain adaptive filter which
performs a novel weighting operation to stabilize the adaptive behavior of
the filter, and which performs the constraint operation directly on the
coefficients of the frequency-domain coefficient vectors. The direct
constraint adaptation approach of the present invention schedules the
direct constraint operation in a highly efficient manner so that only the
most necessary vectors will be constrained while the other vectors allow a
small amount of circular component build-up to accumulate until those
vectors are in turn constrained.
The invention takes advantage of the fact that practically all room
responses are known to have a decaying impulse response. The invention
puts greater "weight" on the short delay coefficients than the longer
delay coefficients in a manner that is approximately proportional to the
strength of the delayed impulse response for a given delay, and uses this
knowledge to make a constraint scheduling mechanism which schedules the
constraint operation on the short delay coefficient vectors with greater
frequency than for the longer delay coefficients.
In principle, the goal is to avoid polluting the coefficients of the
frequency-domain coefficient vectors with any significant degree of
circular coefficient characteristic. The invention meets this goal by
constraining directly on the coefficients of the frequency-domain
coefficient vectors. The weighting scheme has the effect of accelerating
the convergence of the adaptive filter by putting more emphasis on the
shorter delay coefficients. The net effect is that the adaptive filter
converges faster and the coefficients remain practically constrained with
very few computations required for the constraint operation.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows the general configuration of an adaptive echo cancellet (AEC).
FIG. 2 depicts the arrangement of a block frequency-domain adaptive filter
in accordance with a preferred embodiment of the present invention.
FIG. 3 depicts the arrangement of a block frequency-domain adaptive filter
in accordance with another embodiment of the present invention.
FIGS. 4A and 4B detail an embodiment of the constraint operation in
accordance with the present invention.
FIG. 5 details an alternative embodiment of the constraint operation in
accordance with the present invention.
BEST MODE FOR CARRYING OUT THE INVENTION
In general, an acoustic echo canceller (AEC) is an adaptive filter which
utilizes a model of the acoustic response of a room. FIG. 1 shows the
basic components of a typical AEC which employs a frequency-domain
adaptive filter 100. An incoming signal from a far-end caller is sent
along the input line 1, and is broadcast into a room by the loudspeaker 20
of a speakerphone, resulting in the production of echoes 3a, 3b. A
near-end caller speaks into the microphone 22 of the speakerphone,
producing a speech component 2. The echoes 3a, 3b and the speech component
2 are combined in the microphone 22 to form an outgoing signal that is
sent along line 4. The adaptive filter 100 contains a linear model 12 of
the acoustic characteristics of the room, which generates an estimate 6 of
the return echo signals 3a, 3b created in the room. A subtractor 16
subtracts the predicted echo 6 from the outgoing signal 4 to form a
"clean" signal that is sent along the transmit line 8. The clean signal
also serves as the error signal which the adapter 14 uses to adapt the
model 12.
FIGS. 2 and 3 detail preferred embodiments of a frequency-domain adaptive
filter 100 in accordance with the present invention as applied to an AEC.
Following is a description of the elements common to both embodiments,
wherein the common elements between FIGS. 2 and 3 have the same reference
numerals. A discussion of the operation of each embodiment is then
presented, along with additional detail of the elements specific to each
embodiment.
Using FIG. 2 as a reference, a frequency-domain adaptive filter 100 is
shown wherein a received input signal R.sub.in is presented along an input
line 1 in the form of a serial stream of digitized samples of an analog
input signal. A serial to block (S/B) converter 31 gathers the digitized
samples R.sub.in into blocks of N/2 samples, and concatenates a current
block of R.sub.in samples with the previous block of R.sub.in samples to
form an N-sample real vector R.sub.b. A block to serial (B/S) converter 32
and a digital to analog (D/A) converter 33 operate to revert the N-sample
real vectors to an analog signal R.sub.out for broadcast into the room by
a loudspeaker 20. Typical values for N include 64, 128, 256, 512 and 1024
samples, the particular value being based upon design parameters such as
desired system throughput, memory constraints and other hardware
considerations.
Assuming that the room response in the time-domain is denoted by h(t), the
echo resulting from the acoustic reflections of an input signal x(t) is
defined by the convolution of x(t) with h(t). The model 12 of the AEC
filter 100 takes advantage of the fact that a convolution in the
time-domain is equivalent to multiplication in the frequency-domain. The
model 12 includes an array 42 of M frequency-domain coefficient vectors
H.sub.0 ... H.sub.M-1 which approximate the time-domain response h(t).
Each vector H.sub.i represents an estimation of the echo response for an
interval of time. By convention, vector H.sub.0 represents the echo
response for a first interval of time, while vector H.sub.M-1 represents
the echo signal during the M'th time interval. Thus, a vector H.sub.i
represents the echo response delayed by an interval of time with respect
to the previous vector H.sub.i-1. Each vector H.sub.i in the array 42 is
the DFT representation of the time-domain impulse response for the given
time interval and consists of N/2 +1 complex coefficients, known as
"bins," which represent the frequency components of the corresponding
time-domain echo response. The frequency part of each of the 0 (zero) bin
and the N/2 bin is zero, making the storage requirement N words.
The model 12 also includes a first-in, first-out buffer (FIFO) 44. The FIFO
44 has a depth of M input vectors X.sub.0 . . . X.sub.M-1, where each
input vector X.sub.i has a width of N/2+1 bins. A Discrete Fourier
Transform (DFT) 41 transforms the N-sample real vectors R.sub.b into
frequency-domain input vectors X.sub.i which by use of symmetry properties
are of size N/2+1 complex values, and are stored in the FIFO 44. In
digital processors, the FIFO is normally implemented as a circular queue
in RAM. The most efficient order of storage is dependent upon the
peculiarities of the digital processor used. In general, however, when an
input vector X.sub.i is created and pushed onto the FIFO 44, it is marked
as block index zero (0) with subsequent input vectors having their index
incremented by one.
A multiplication means 43 and summing means 45 provide for the
frequency-domain equivalent of the time-domain convolution of x(t) and
h(t). The resulting echo vector Y is an estimate, in the frequency-domain,
of the actual echo produced in the room. An inverse DFT (IDFT) 47 converts
the echo vector Y into a time-domain real vector of N/2 linearly convolved
samples appearing at a summing means 16.
Although a fast Fourier transform (FFT) is contemplated for the DFTs, no
specific implementation of DFT is required. Likewise, an inverse FFT is
contemplated for the IDFT 47, but is not restricted to any particular
implementation of IDFT. It is noted that persons skilled in the art will
realize that a Hilbert transformation can be applied to convert the
time-domain input data into complex numbers prior to transformation to the
frequency-domain by the DFT 41, since FFTs generally operate more
efficiently with complex data.
A microphone 22 picks up the near-end talker speech and the echoes produced
in the room, and generates a time-domain outgoing signal s(t). An A/D
converter 34 generates a stream of digitized samples S.sub.in, which are
then organized into N/2 linearly convolved sample blocks by an S/B
converter 35. The summing means 16 subtracts the N/2 sample blocks of the
approximated echo Y from the N/2 sample blocks of S.sub.in to produce
digitized samples of a "clean" signal S.sub.out. The clean signal
S.sub.out follows two paths: The signal continues along the transmit line
8 for subsequent transmission to the far-end caller. At the same time, the
signal serves as an error signal e.sub.b which the filter 100 uses to
adapt the model 12.
The adaptation means 14 consists of a DFT 63 which produces a
frequency-domain error vector E from the time-domain digitized error
signal e.sub.b. The error vector E is correlated with the input vectors
X.sub.i, stored in the FIFO 44, through operation of the conjugation means
64 and the step-size adjustment means 66. Each frequency-domain
coefficient vector H.sub.i is adapted by the results of the
cross-correlation through a summing means 67. Finally, the constraint
scheduler 60 subjects certain ones of the adapted frequency-domain
coefficient vectors H.sub.i in the array 42 to a constraint operator 62.
The discussion will now turn to the operation of an AEC adaptive filter in
accordance with the present invention as shown in the preferred embodiment
of FIG. 2. In general, the sequencing of processing in the filter 100 is
governed by the synchronized arrival of the digitized samples at R.sub.in
and S.sub.in of the received input signal and microphone input signal
respectively. When a new block of N/2 samples is received, the convolution
operation in the model 12 commences, resulting in the generation of a
"clean" signal S.sub.out, which is then queued along the path 8 and
eventually to the far-end. In line with the path 8, there may be
additional post-processing of the clean signal S.sub.out to further
enhance the signal.
The adaptation portion of the filtering process commences upon receipt of
the "clean" signal S.sub.out, now referred to as the error signal e.sub.b,
at the adaptation means 14. The adaptation means 14 is responsible for
adapting the coefficient vectors H.sub.i in the array 42, and for
constraining the coefficient vectors according to a constraint scheduler
60. The adaptation is performed in the time period that remains while the
next N/2 samples are being gathered and queued into blocks by the S/B
converter 31 and 35. The number N/2 is known as a "block period." The AEC
filter of the present invention typically operates with a cycle time of
about 125 microseconds, corresponding to an 8000 Hz operating frequency.
When a new block of N/2 R.sub.in samples is received, it is concatenated
with the previously received block to form an N-sample real vector
R.sub.b, which is then transformed into the frequency-domain via DFT 41,
and pushed onto the FIFO 44. This action initiates the convolution
operation to produce an approximation Y of the actual echo produced in the
room.
The convolution operation of the preferred embodiment of the present
invention differs from the conventional structure of previous block
frequency-domain adaptive filters described in the literature. FIG. 2
shows a weight adjustment means 40 for adjusting the coefficient vectors
H.sub.i, each coefficient vector H.sub.i having a corresponding weight
adjustment vector W.sub.i. The convolution operation, in accordance with
the present invention, begins by multiplying the N/2+1 bins h.sub.ib of
each vector H.sub.i by the corresponding weight adjustment W.sub.ib, The
bins of these adjusted coefficient vectors are then multiplied by the
corresponding bins of the input vectors X.sub.i. The resulting final
product terms for each bin are summed by the summing means 45 to produce a
single frequency-domain vector of length N/2+1, which is the
frequency-domain approximation of the echo response y. The following
equations describe the operation:
##EQU1##
where * in the vector equation (la) represents element-by-element
multiplication,
M is the depth of the FIFO,
b is the bin index and b=0 . . . N/2,
i is the FIFO index,
M.sub.b is the FIFO depth for a given bin index and
Y.sub.b is the echo coefficient value for a given bin index,
h.sub.ib is the complex coefficient value for a given FIFO index and bin
index,
x.sub.ib is the complex FIFO value for a given FIFO index and bin index,
and
w.sub.ib is the weight adjustment for a given FIFO index and bin index.
It may not be necessary to obtain the product terms for all of the input
vectors X.sub.i in the FIFO 44 for a given bin. For example, the energy in
a given bin (i.e. a given frequency component) for the longer delayed
input vectors may be so attenuated that they can be ignored. This presents
an opportunity to save on computations by limiting the depth of the FIFO
44 for certain bins when performing the convolution. The value M.sub.b
controls the depth of the FIFO 44 by taking on different values between 0
and M-1 for each bin index b. It is generally noted that performance of
the convolution operation can be affected by having different values for
M.sub.b, and that other criteria for selecting M.sub.b values are
possible.
The weight adjustment means 40 is based on the fact that acoustic echo,
i.e. the impulse response, in ordinarily constructed rooms will always
decay. The weight adjustments W.sub.ib are determined based on the
following a priori assumptions about the expected impulse response:
1) The direct coupling between the loudspeaker and the microphone has a
transfer function, i.e. impulse response, that is independent and
separable from the indirect coupling.
2) The envelope of the impulse response of indirect coupling, i.e. echo
from walls, furniture, etc. which do not have a direct path from the
loudspeaker to the microphone, has the characteristic that it decays in an
exponential fashion.
3) The direct coupling impulse response is principally a function of the
physical distance between the loudspeaker and the microphone, the queuing
delay of A/D and D/A | | |
