[0001] The invention is directed to a handsfree system for use in a vehicle comprising a
microphone array with at least two microphones and a signal processing means.
[0002] For making telephone calls in a car, handsfree systems are used more and more since
they provide increased comfort and reduce the risk of an accident as the driver is
distracted only marginally. Because of that, in many countries, handsfree devices
are even required by law.
[0003] Usually, a handsfree system comprises a microphone that can be fastened to a user
such as the driver.
[0004] Due to the relatively large distance between the speaker's mouth and the microphone,
many handsfree devices today suffer from the drawback of a poor speech quality. This
is particularly due to the fact that in a car, usually a large ambient noise is present
interfering with the speech signal. The noise stems from different sources such as
the motor, wind, or car radio.
[0005] However, common methods for noise reduction are often costly to implement and require
a large amount of memory and computing power. In particular, a signal processed by
conventional noise reduction systems has a relatively large delay time which makes
these systems unsuitable for real time applications, i.e. telephone applications.
[0006] It is, therefore, the problem underlying the invention to overcome the above drawbacks
and provide a handsfree system for use in a vehicle with improved speech quality.
[0007] This problem is solved by a handsfree system according to claim 1. Accordingly, the
invention provides a handsfree system for use in a vehicle comprising a microphone
array with at least two microphones, a signal processing means and an adaptive post-filter,
the signal processing means comprising a beamformer having an input connected to the
at least two microphones and an output connected to the input of the adaptive post-filter.
[0008] In the context of this invention, the term "connected" also includes the case that
a filter or another signal processing means is provided along the signal path between
two devices or means. A beamformer processes signals emanating from a microphone array
to obtain a combined signal. A beamformer comprises a beamsteering means being responsible
for time delay compensation of the different microphones and a summing means. In its
simplest form (Delay-and-Sum beamformer), beamforming only comprises delay compensation
and summing of the compensated signals. Beamforming allows to provide a specific directivity
pattern for a microphone array. Usually, a beamformer can be implemented as digital
system with a plurality of digital filter using, for example, digital signal processors
(DSP). A beamformer can be configured as an adaptive or a non-adaptive beamformer.
Adaptive means that relevant parameters such as filter coefficients can be re-calculated
during use of the system in order to adapt the beamformer to changing conditions.
In the non-adaptive case, the system parameters are determined once by calibrating
the beamformer and, then, kept unchanged. In both cases of a non-adaptive and an adaptive
beamformer, the beamforming, in principle, can be performed in the time domain or
in the frequency domain.
[0009] A handsfree system in accordance with the invention shows an excellent acoustic performance
in a vehicular environment. Due to the beamformer, an improved directivity is obtained
and, furthermore, speech signals are enhanced and ambient noise is reduced. The adaptive
post-filter (responsible for filtering a signal after the beamforming) further reduces
the noise in the signal.
[0010] According to a preferred embodiment, the adaptive post-filter can be a filter in
the time domain. If the post-filtering is performed in the time domain, the delay
time is reduced and the implementation is simplified.
[0011] According to a preferred embodiment, the adaptive post-filter can be a Wiener filter.
It turns out that a Wiener filter is particularly suitable for filtering in a car
environment.
[0012] In order to reduce spectral distortions of the filtered signal, preferably, the adaptive
post-filter can be a linear-phase filter. Advantageously, the adaptive post-filter
can be a linear-phase Wiener filter.
[0013] According to a preferred embodiment, the signal processing means can further comprise
at least two adaptive filters having an input connected to the output of the beamsteering
means and an output connected to the adaptive post-filter, wherein the at least two
adaptive filters are configured to determine adaptive filter parameters for the adaptive
post-filter.
[0014] In this way, background filters are provided for adaptively estimating the filter
parameters for the adaptive post-filter.
[0015] Preferably, for each of the at least two microphones, an adaptive filter can be provided
having an input connected to the output of the beamsteering means. Thus, for each
output of the beamsteering signal corresponding to a microphone, adaptive filter parameters
can be determined for the adaptive post-filter. The actual filter parameters of the
post-filter can be given, for example, by the filter parameters determined by one
of the adaptive filters or the mean of the filter parameters determined by several
different adaptive filters.
[0016] Advantageously, an input of each of the at least two adaptive filters can be further
connected to the output of the beamformer. This allows for an adaption of the respective
filter parameters directly with respect to the beamformed signal.
[0017] According to a preferred embodiment, the signal processing means can further comprise
a pre-emphasis filter, in particular, comprising a pre-whitening filter, having an
input connected to an output of the adaptive post-filter and/or a pre-emphasis filter,
in particular, comprising a pre-whitening filter, having an input connected to the
output of the beamsteering means and an output connected to the at least two adaptive
filters.
[0018] Such a pre-emphasis filter, on the one hand, emphasizes high frequencies and, on
the other hand, attenuates low frequencies which is particularly useful to reduce
low frequency correlated noise. Preferably, the pre-emphasis filter can comprise a
pre-whitening filter. A pre-whitening filter whitens the spectral distribution of
a signal. The filter coefficients of such a pre-whitening filter can be determined
using a linear predictive coding (LPC) analysis, for example, via an adaptive lattice
predictor (ALP) algorithm.
[0019] According to a preferred embodiment of the above handsfree systems, the signal processing
means can further comprise an inverse filter, particularly a warped inverse filter.
These filters are especially useful to adjust the microphone transfer function and
to match the microphones of the array in this way. Preferably, the beamformer can
comprise at least one inverse filter, in particular, having an output for providing
an inversely filtered signal to a summing means.
[0020] In order to overcome the matching problem, alternatively or additionally, matched
microphones on the basis of silicone or paired microphones may be used.
[0021] The susceptibility of microphone arrays often increases with decreasing frequency.
Due to this, a higher matching precision is preferred for low frequencies compared
to high frequencies. A frequency depending adjustment of the microphone transfer functions
with the use of warped filters reduces the required memory compared to the case of
conventional FIR filters.
[0022] Preferably, each inverse filter can be an approximate inverse of a non-minimum phase
filter. This results in an inverse filter which is both stable and has no phase error.
[0023] According to a preferred embodiment, an inverse filter may be combined with another
filter of the handsfree system, for example, a filter of the beamformer. Such a combination
in one filter results in a simplified implementation.
[0024] Preferably, the signal processing means of the above handsfree systems can comprise
a non-adaptive post-filter having an input connected to an output of the adaptive
post-filter. The non-adaptive post-filter may directly follow the adaptive post-filter.
Such a filter is used to compensate for the ambient acoustics of a speaker. Thus,
the non-adaptive post-filter may have the form of an inverse room filter.
[0025] In order to further reduce low frequency noise, according to a preferred embodiment,
the signal processing means may further comprise an adaptive noise canceller (ANC),
for electrical ANC implementations.
[0026] Preferably, the ANC can be connected to a non-acoustic sensor to determine a noise
signal, for example, by using the tachometer of the vehicle. The ANC, advantageously,
can have an output connected to the input of the beamformer and/or of the adaptive
post-filter.
[0027] For a further improvement of the speech signal quality, the signal processing means
of the previously described handsfree systems can comprise an acoustic echo canceller
AEC. Preferably, the AEC can comprise an echo shaping filter. In this way, a frequency
selected echo attenuation may be obtained. As in the case of an ANC, the AEC can have
an output connected to the input of the beamformer and/or of the adaptive post-filter.
[0028] According to a preferred embodiment of all previously described handsfree systems,
the beamformer can be a non-adaptive beamformer. By using a non-adaptive beamformer
with fixed filters, the computing power during operation of the system is reduced.
[0029] Preferably, the beamformer may be a superdirective beamformer which further improves
the acoustic performance.
[0030] Advantageously, the beamformer may be a regularized superdirective beamformer using
a finite regularization parameter µ. The regularization parameter usually enters the
equation for computing the filter coefficients or, alternatively, is inserted into
the cross-power spectrum matrix or the coherence matrix. In contrast to the maximum
superdirective beamformer (µ = 0), the regularized superdirective beamformer has reduced
noise and is less sensitive to an imperfect matching of the microphones.
[0031] The finite regularization parameter µ, preferably, may depend on the frequency. This
achieves an improved gain of the array compared to a regularized superdirective beamformer
with fixed regularization parameter µ. According to a preferred embodiment, each superdirective
filter may result from an iterative design based on a predetermined maximum susceptibility.
This enables an optimal adjustment of the microphones, particularly with respect to
the transfer function and the position of each microphone.
[0032] By using a predetermined maximum susceptibility, defective parameters of the microphone
array can be taken into account to further improve the gain. The maximum susceptibility
may be determined as a function of the error in the transfer characteristic of the
microphones, the error in the microphone positions and a predetermined (required)
maximum deviation in the directional diagram of the microphone array. The time-invariant
impulse response of the filters will be determined iteratively only once; there is
no adaption of the filter coefficients during operation.
[0033] According to a preferred embodiment, each superdirective filter can be a filter in
the time domain. Filtering in the frequency domain is a possible alternative, however,
requiring to perform a Fourier transform (FFT) and an inverse Fourier transform (IFFT),
thus, increasing the required memory.
[0034] Advantageously, the beamformer may have the structure of a generalized sidelobe canceller
(GSC). In this way, at least one filter can be saved. The implementation in the GSC
structure, however, is only possible in the frequency domain.
[0035] In order to obtain an optimal adaption of the handsfree system to a particular noise
situation, according to a preferred embodiment, the beamformer can be a minimum variance
distortionless response (MVDR) beamformer.
[0036] According to a preferred embodiment, the microphone array can comprise at least two
microphones being arranged in endfire orientation with respect to a first position.
An array in endfire orientation has a better directivity and is less sensitive to
a mismatched propagation or delay time compensation. The first position can be the
location of the drivers head, for example.
[0037] Preferably, the microphone array can comprise at least two microphones being arranged
in endfire orientation with respect to a second position. Thus, the handsfree system
of the invention has a good directivity in two directions. Speech signals coming from
two different positions, for example, from the driver and the front seat passenger,
can both be recorded in good quality.
[0038] According to a preferred embodiment, the signal processing means may comprise at
least two beamformers. A first beamformer may be used for signals from a first position
and a second beamformer may be used for signals from a second position. In this case,
advantageously, the handsfree system may further comprise a voice activity detector
(VAD) and/or a switch control means. The switch control and the VAD are used to determine
how to combine the output of the at least two beamformers.
[0039] Advantageously, the handsfree system can comprise a residual echo suppression (RES)
means and/or a dynamic volume control (DVC). A RES means serves for suppression of
residual echoes, in particular, being present in the signal resulting from the adaptive
post filter. Thus, a residual echo suppression means can comprise an input connected
to the output of the adaptive post filter. Furthermore, a RES means can comprise an
input for receiving a far end signal. A DVC is intended for dynamically adapting the
output volume of a far end signal depending on the ambient noise level being present
in the vehicle.
[0040] According to a preferred embodiment, the at least two microphones in the first endfire
orientation (endfire orientation with respect to a first position) and the at least
two microphones in the second endfire orientation (endfire orientation with respect
to a second position) can have a microphone in common. In this way, already a microphone
array consisting of only three microphones provides an excellent directivity for use
in a vehicular environment.
[0041] According to a preferred embodiment of all previously discussed handsfree systems,
the microphone array may comprise at least two subarrays. Each subarray of microphones
may be optimized for a specific frequency band yielding an improved overall directivity.
[0042] To decrease the total number of microphones, preferably, at least two subarrays may
have at least one microphone in common.
[0043] According to a preferred embodiment, the above handsfree systems may comprise a frame
wherein each microphone of the microphone array is arranged in a predetermined, in
particular fixed, position in or on the frame. This ensures that after manufacture
of the frame with the microphone, the relative positions of the microphones are known.
Such an array can be easily mounted in a vehicular cabin.
[0044] According to a preferred embodiment, at least one microphone may be a directional
microphone. The use of directional microphones improves the array gain.
[0045] Preferably, at least one directional microphone may have a cardioid characteristic.
This further improves the array gain. More preferred, the cardioid characteristic
is a hyper-cardioid characteristic.
[0046] Advantageously, at least one directional microphone may be a differential microphone.
This results in a microphone array with excellent directivity and small dimensions,
in particular, the differential microphone may be a first order differential microphone.
[0047] The invention is further directed to a vehicle, particularly a car, comprising any
of the above-described handsfree systems.
[0048] The invention is also directed to the use of any of the previously described handsfree
systems in a vehicle, in particular, a car.
[0049] Furthermore, the invention provides a method for noise reduction in a vehicular handsfree
system, comprising receiving input signals resulting from a microphone array with
at least two microphones, processing the input signals by a beamformer to provide
a beamformed signal, and adaptively filtering a signal resulting from the beamformed
signal by an adaptive post-filter.
[0050] This method results in an excellent acoustic performance of a handsfree system in
a vehicular environment.
[0051] According to a preferred embodiment, the adaptive filtering can be performed in the
time domain. In this way, particularly the delay time is reduced.
[0052] Preferably, the method can further comprise
providing at least two adaptive filters, particularly Wiener filters, wherein
beam processing the input signals by a beamformer forming comprises beamsteering the
input signals for providing beamsteered signals corresponding to one of the at least
two microphones and summing the signals, and
adaptively filtering comprises receiving and processing at least one beamsteered signal
by at least one of the at least two adaptive filters to determine adaptive filter
parameters for the adaptive post filter.
[0053] According to a preferred embodiment, adaptively filtering can further comprise receiving
a signal resulting from the beamformed signal by at least one adaptive filter and
wherein processing the beamsteered signal can comprise determining adaptive filter
parameters using the at least one beamsteered signal and the signal resulting from
the beamformed signal.
[0054] Preferably, for each beamsteered signal, an adaptive filter can be provided for determining
adaptive filter parameters using the beamsteered signal and the signal resulting from
the beamformed signal.
[0055] In order to reduce low frequency correlated noise, receiving at least one beamsteered
signal by at least one of the at least two adaptive filters can comprise processing
the at least one beamsteered signal by a pre-emphasis filter, in particular, comprising
a pre-whitening filter.
[0056] According to an advantageous embodiment, the above methods can further comprise processing
a signal resulting from the microphone array by an inverse filter, in particular,
a warped inverse filter.
[0057] Preferably, the methods can further comprise non-adaptively filtering a signal resulting
from the adaptively filtered signal and/or processing a signal resulting from the
adaptively filtered signal by a pre-emphasis filter.
[0058] The above method, advantageously, can further comprise processing a signal resulting
from the microphone array, particularly resulting from the beamformed signal, by an
adaptive noise canceller (ANC) and/or an acoustic echo canceller (AEC) and/or a residual
echo suppression (RES) means.
[0059] According to a preferred embodiment, the input signals can be processed by a non-adaptive
and/or superdirective and/or minimum variance distortionless response (MVDR) beamformer.
[0060] The invention also provides a computer program product comprising one or more computer
readable media having computer-executable instructions for performing the steps of
the above described methods.
[0061] Additional features and advantages will be described with reference to the examples
illustrated in the drawings:
- Fig. 1
- illustrates the structure of a handsfree system according to the invention with an
adaptive post-filter in the time domain;
- Fig. 2
- shows the structure of a beamformer in the frequency domain;
- Fig. 3
- illustrates an FXLMS algorithm;
- Fig. 4
- shows the structure of a beamformer in the time domain;
- Figs. 5A, 5B
- illustrate preferred embodiments of arrangements of the microphone array in a vehicle;
- Figs. 6A, 6B
- illustrate preferred embodiments of arrangements of a microphone array in a mirror;
- Fig. 7
- shows a microphone array consisting of three subarray;
- Fig. 8
- illustrates a superdirective beamformer in a GSC structure;
- Fig. 9
- illustrates a microphone array with two microphones in a noise field with a noise
free sector;
- Fig. 10
- shows the structure of a superdirective beamformer comprising four first order gradient
microphones;
- Fig. 11
- illustrates the structure of a handsfree system with an electrical ANC;
- Fig. 12
- shows the structure of an ANC;
- Fig. 13
- shows the structure of an embodiment of a handsfree system according to the invention
with an ANC and AEC;
- Fig. 14
- illustrates the structure of an AEC; and
- Fig. 15
- shows another embodiment of a handsfree system according to the invention.
[0062] An example of the handsfree system in accordance with the present invention is shown
in Fig. 1. In the following, first, the general structure will be shortly described,
and, then, the different components will be explained in more detail. In the figures,
it is to be noted that the dotted lines encasing some elements simply serve for better
understanding of the figures without necessarily implying any actual combination or
separation of different elements.
[0063] The main components of the system are a microphone array, a beamformer and an adaptive
post-filter in the time domain. The microphone array 101, in this example, comprises
four microphones 102. Each microphone 102 yields an output signal x
i[
k]. The microphone signals may be filtered by an optional high-pass filter 103.
[0064] Then, the signals are passed to a beamformer. This beamformer may be a conventional
delay and sum beamformer. However, in the present example, a preferred superdirective
beamformer is shown. Such a beamformer comprises beamsteering means 104 and filters
105. The output signals of the beamformer may be passed through optional inverse filters
106 and, then, are summed by summing means 107 to yield a resulting beamformed signal
x[
k].
[0065] This signal is passed through an adaptive post-filter 108 in the time domain which
may be followed by an optional non-adaptive post-filter 109 and/or by an optional
pre-emphasis filter (not shown). The adaption of the post-filter 108 is performed
using a set of Wiener filters 109. The input signals of the Wiener filters 110 comprise,
on the one hand, the individual signals resulting from the different microphones and,
on the other hand, the summed signal
x[
k]. In the present example, the microphone signals are taken after the beamsteering.
However, if the beamformer comprises further (superdirective) filters 105 as in the
present case, it is also possible to take the microphone signals after this additional
filtering. Before being presented to the Wiener filters, the microphone signals are
passed through an optional pre-emphasis filter 111.
[0066] In the following, the functioning of a Wiener filter will be explained. A microphone
signal
x[
k] is the sum of the speech signal
s[
k] and the noise
n[
k]. The microphone signal will be filtered by an impulse response
w(
i) to obtain a noise reduced signal

[
k]. It is the aim to minimize the mean square error between the undisturbed speech
signal
s[
k] and the output signal

[
k]:

[0067] In other words, the partial derivative of the mean square error with respect to the
coefficients of the impulse response has to vanish yielding the Wiener-Hopf equation:

wherein
rxx(
l) and
rsx(
l) are the auto-correlation function and the cross-correlation function of the microphone
signal and the undisturbed speech signal. One may assume that the speech signal and
the noise are statistically independent, i.e.
rsx(
l) =
rss(
l), thus,

[0068] A transformation of this equation into the frequency domain yields the frequency
response of the Wiener filter:

[0069] In order to obtain a time variant filter, the power spectral densities in the above
equation may be replaced by the corresponding short-time estimated values that may
be obtained, for example, by a recursive averaging:

wherein
S(
κ,ν) and
X(
κ,ν) are short-time spectra that may be determined, for example, with the help of DFT
filter banks or an FFT. Here, κ is the time index and ν the frequency index;

{.} represents the short-time average that may be obtained, for example, with the
help of a first order IIR filter.
[0070] The short-time auto power spectral density of the speech signal in the numerator
of the above equation is to be estimated in a suitable way. Appropriate estimation
methods include spectral subtraction (estimating the auto power spectral density of
the noise), minimum mean square error short-time spectral amplitude (MMSE STSA) estimator
or MMSE log-SA estimator or a speech pause detector, for example.
[0071] It is also possible to estimate the short-time auto power spectral density of the
noise signal with the help of the coherence between two or more microphones. In a
second step, the estimated short-time auto power spectral density of the noise signal
may be used to estimate the absolute value of the most probable Fourier coefficient
(using, for example, a spectral subtraction algorithm or an MMSE log-SA estimator)
and to reconstruct the absolute value of the spectrum of the speech signal. For the
multi-channel noise reduction, one estimates that the coherence or the cross power
spectral density of the noise signals received by the microphones is vanishing. In
the case of two microphones, for example, the microphone signal has the form:

wherein
h1(
i) and
h2(
i) are the impulse responses representing the acoustic transfer between the source
of speech and the microphones. Both parts of the speech signal filtered in this way
are superimposed with the uncorrelated noise signals
n1[
k] and
n2[
k].
[0072] Since Φ
n1n2 (
ω) = 0 and assuming that the Fourier transforms (
H1(
ω) and
H2 (
ω)) of the impulse responses of the acoustic transfer (
h1(
i) and
h2(
i)) obey |
H1 (
ω)| = |
H2 (ω)|, one obtains for the short-time auto power spectral density

wherein

[0073] The corresponding Wiener filter, then, has the form

[0074] In Fig. 1, the adaption of the post-filter
w(
k,i) -
k being the time index and
i denoting the coefficient within the impulse response - is performed in the time domain,
for example, with the help of the LMS algorithm. The background Wiener filters
w1(
k,
i),...,
w4(
k,
i) are two minimize the error signals
e1[
k],...,
e4[
k] such that, for example, the filter
w4(
k,
i) tends towards the frequency response

wherein

[0075] The form of the other three Wiener filters is obtained by a cyclic permutation of
the indices.
[0076] It is to be understood that the system is not restricted to a particular number of
Wiener filters 110. Furthermore, not every Wiener filter 110 is always to be used
to determine the adaptive post-filter 108. For example, one may use only the Wiener
filter which uses the microphone signal of the microphone proximal to the source of
speech.
[0077] Preferably, however, the adaptive post-filter 108 is determined as

[0078] The filter is linear-phase if the filter coefficients satisfy

[0079] Using this symmetry condition, the filter coefficients of a linear-phase post-filter
(with length L) can be obtained. Accordingly, the linear-phase post-filter has twice
the length of one of the background filters 110 (with length L/2). Such a linear-phase
filter only modifies the amplitude spectrum of the input signal of the filter without
a frequency dependent distortion of the phase spectrum.
[0080] The performance of the filter can be further improved by smoothing its frequency
response. This can be achieved by weighting the filter coefficients with a window
function.
[0081] The inverse filters 106 serve to compensate for the acoustic transfer function of
the path between the source of speech and the microphones.
[0082] In Figure 2, the structure of a superdirective beamformer is shown. The beamformer
shown in this figure performs the filtering in the frequency domain, in contrast to
the case of Figure 1. If a beamformer in the frequency domain were used in Figure
1, an inverse Fourier transform is to be performed on the signals before passing the
signals to the Wiener filters 110 or the pre-emphasis filter 111.
[0083] In Figure 2, the microphone array consists of M microphones 102, each yielding a
signal x
i(t). The signals x
i(t) are transferred to the frequency domain by fast Fourier transform (FFT) means
201, resulting in a signal X
i(ω). In general, the beamforming consists of a beamsteering and a filtering. The beamsteering
is responsible for the propagation time compensation. The beamsteering is performed
by a steering vector

with

and

wherein
pref denotes the position of a reference microphone,
pn the position of microphone
n, q the position of the source of sound (for example, the speaker),
f the frequency and
c the velocity of sound. In the far field, one has

[0084] According to a rule of thumb, one has the far field situation if the source of the
useful signal is more than twice as far from the microphone array as the maximum dimension
of the array. In Figure 2, a far field beamformer is shown since only a phase factor
e
jωτk denoted by reference sign 202 is applied to the signals
Xk(
ω).
[0085] After the beamsteering, the signals are filtered by superdirective filters 203 that
are filters in the frequency domain. The filtered signals are summed yielding a signal
Y(ω). After an inverse fast Fourier transform (IFFT) by means 204, the resulting signal
y[
k] is obtained.
[0086] The optimal filter coefficients
Ai (ω) may be computed according to

wherein the superscript
H denotes Hermitian transposing and Γ(
ω) is the complex coherence matrix

the entries of which are the coherence functions that are defined as the normalized
cross-power spectral density of two signals

[0087] Preferably, the beamsteering is separated from the filtering step which reduces the
steering vector in the design equation for the filter coefficients
Ai (
ω) to the unity vector

(The superscript T denotes transposing.)
[0088] In the case of an isotropic noise field in three dimensions (diffuse noise field),
the coherence is given by

with
si(
x) =

and wherein
dij denotes the distance between microphones
i and
j and Θ
0 is the angle of the main receiving direction of the microphone array or the beamformer.
[0089] The above described design rule for computing the optimal filter coefficients
Ai (
ω) for a homogenous diffuse noise field is based on the assumption that the microphones
are perfectly matched, i.e. point-like microphones having exactly the same transfer
function. In practice, therefore, a so-called regularized filter design may be used
to adjust the filter coefficients. To achieve this, a scalar (the regularization parameter
µ) is added at the main diagonal of the cross-correlation matrix. In a slightly modified
version, all elements of the coherence matrix not on the main diagonal are divided
by (1 + µ):

[0090] Alternatively, the regularization parameter µ may be introduced into the equation
for computing the filter coefficients:

wherein
I is the unity matrix. For convenience, in the following, the second approach where
the regularization parameter is part of the filter equation will be discussed in more
detail. It is to be understood, however, that the first approach is equally suitable.
[0091] Before discussing the superdirective beamformer in more detail, some characteristic
quantities of a microphone array are to be defined. The directional diagram or response
pattern (Ψ(ω,Θ) of a microphone array characterizes the sensitivity of the array as
a function of the direction of incidence Θ for different frequencies .
[0092] A measure to describe the directivity of an array is the so-called gain that does
not depend on the angle of incidence Θ. The gain is defined as the sensitivity of
the array in the main direction of incidence with respect to the sensitivity for omnidirectional
incidence.
[0093] The Front-To-Back-Ratio (FBR) indicates the sensitivity in front receiving direction
compared to the back.
[0094] The white noise gain (WNG) describes the ability of the array to suppress uncorrelated
noise, for example, the inherent noise of the microphones. The inverse of the white
noise gain is the susceptibility
K(
ω),
[0095] The susceptibility
K(
ω) describes the array's sensitivity to defective parameters. It is often preferred
that the susceptibility
K(
ω) of the array filters
Ai(
ω) does not exceed an upper bound
Kmax(
ω)
. The selection of this upper bound may be dependent on the relative error Δ
2(ω, Θ) of the microphones and, for example, on requirements regarding the directional
diagram Ψ(ω, Θ). The relative error Δ
2(ω,Θ), in general, is the sum of the mean square error of the transfer properties
of all microphones
ε2(
ω,Θ) and the Gaussian error with zero mean of the microphone positions δ
2(ω).
[0096] Defective array parameters may also disturb the ideal directional diagram; the corresponding
error can be given by
Δ2(
ω, Θ)
K(
ω). If one requires that the deviations in the directional diagram do not exceed an
upper bound of ΔΨ
max(ω, Θ), one obtains for the maximum susceptibility:

[0097] It is to be noted that in many cases the dependence on the angle Θ can be neglected.
[0098] In practice, the error in the microphone transfer functions ε(ω) has a higher influence
on the maximum susceptibility
Kmax(
ω) and, thus, also on the maximum possible gain
G(
ω) than the error
δ2(ω) in the microphone positions. In other words, the defective transfer functions are
mainly responsible for the limitation of the maximum susceptibility.
[0099] A higher mechanical precision to reduce the position deviations of the microphones
is only sensible up to a certain point since the microphones usually are modeled as
being point-like, which is not true in reality. Thus, one can fix the positioning
errors
δ2(
ω) to a specific value, even if a higher mechanical precision could be achieved. For
example, one can take
δ2(
ω)
=1% which is quite realistic. The error ε(ω) can be derived from the frequency depending
deviations of the microphone transfer functions.
[0100] To compensate the above-mentioned errors, inverse filters may be used to adjust the
individual microphone transfer functions to a reference transfer function. Such a
reference transfer function can be the transfer function of one microphone out of
the array or, for example, the mean of all measured transfer functions. In case of
the first possibility, only
M -1 inverse filters (
M being the number of microphones) are to be computed and implemented.
[0101] In general, the transfer functions are not minimal phase, thus, a direct inversion
would yield instable filters. Usually, one inverts only the minimum phase part of
the transfer function (resulting in a phase error) or one inverts the ideal (non-minimum
phase) filter only approximately. In the following, the approximate inversion with
the help of an FXLMS (filtered X least mean square) or the FXNLMS (filtered X normalized
least mean square) algorithm will be described.
[0102] After computing of the inverse filters, they may be coupled with the superdirective
filters
Ai(ω) such that, in the end, only one filter per viewing direction and microphone is
to be implemented.
[0103] The FXLMS or the FXNLMS algorithm is described with reference to Figure 3. The error
signal
e[
n] at time
n is calculated according to

with the input signal vector

wherein
L denotes the filter length of the inverse filter
W(z). The filter coefficient vector of the inverse filter has the form

the filter coefficient vector of the reference transfer function P(z)

and the filter coefficient vector of the
n-th microphone transfer function
s(
z)

[0104] The update of the filter coefficients of w[
n] is performed iteratively, i.e. at each time step
n , whereby the filter coefficient w[
n] are computed such that the instantaneous squared error
e2[
n] is minimized. This can be achieved, for example, by using the LMS algorithm:

or by using the NLMS algorithm

wherein µ characterizes the adaption steps and

denotes the input signal vector filtered by
S(
z).
[0105] In general, the susceptibility increases with decreasing frequency. Thus, it is preferred
to adjust the microphone transfer functions depending on frequency, in particular,
with a high precision for low frequencies. To achieve a high precision of the inverse
filters, the FIR filters, for example, are to be very long in order to obtain a sufficient
frequency resolution in the desired frequency range. This means that the expenditure,
in particular, regarding the memory, increases rapidly. When using a reduced sampling
frequency of, for example,
fa =
8kHz, the computing time does not impose a severe limitation. A suitable frequency depending
adaption of the transfer functions can be achieved by using short WFIR filters (warped
filters).
[0106] One possible iterative method to design the filters
Ai(
ω) with predetermined susceptibility goes as follows:
1. Set µ(ω) = 1.
2. Determine the transfer functions of the filters Ai(ω) and the resulting susceptibilities K(ω) according to the equations:

and

3. If the susceptibility K(ω) is larger than the maximum susceptibility (K(ω) > (Kmax (ω)), increase µ in the following step, otherwise, decrease µ.
4. Repeat steps 2 and 3 until the susceptibility K(ω) is sufficiently close to the predetermined value Kmax(ω). The iteration is to break off if µ becomes smaller than a lower limit of, for example,
µmin =10-8 . Such a termination criterion is mainly necessary for high frequencies f ≥cl(2dmic).
[0107] Of course, there are other possibilities to compute the filters
Ai(
ω). For example, one can use a fixed parameter µ for all frequencies. This simplifies
the computation of the filter coefficients. It is to be noted that the above iterative
method is not used for a real time adaption of the filter coefficients during operation.
[0108] A realization of the beamforming filters in the time domain - as in the system of
Figure 1 - is described with reference to Figure 4. Signals are recorded by microphones
102. A near field beamsteering 104 is performed using gain factors ν
k 401 to compensate for the amplitude differences and time delays τ
k 402 to compensate for the propagation time differences of the microphone signals
xk[
i] The realization of the superdirective beamforming is achieved using the filters
(preferably, FIR filters)
αk(
i) indicated by reference sign 403.
[0109] The impulse responses α
1(
i),...,
αM(
i) can be determined as follows:
1. Determine the frequency responses Ai(ω) according to the above equation.
2. To obtain real valued impulse responses α1(i),...,αM(i), chose the frequency responses above half of the sampling frequency to (Ai(ω) = A

(ωA-ω)) with ωA denoting the sampling angular frequency.
3. Transfer these frequency responses to the time domain using an IFFT yielding the
desired FIR filter coefficients α1(i),...,αM(i).
4. Applying a window function, for example, a Hamming window, to the FIR filter coefficients
a1 (i),...,αM(i).
[0110] As can be seen in Figure 4, in contrast to the beamforming in the frequency domain
as described above, the microphone signals are directly processed using the beamsteering
104 in the time domain. The beamsteering 104 is followed by the FIR filtering 403.
After summing the filtered signals, a resulting enhanced signal
y[
k] is obtained.
[0111] Depending on the distance between speaker and microphone array, on the distance between
the microphones themselves, and on the sampling frequency
fa, more or less propagation or transit time between the microphone signals is to be
compensated. The following equation is to be taken into account:

[0112] The higher the sampling frequency
fa or the higher the distance between adjacent microphones, the more transit time Δ
max (in taps of delay) is to be compensated for. The number of taps increases also if
the distance between speaker and microphone arrays is decreased. In the near field,
more transit time is to be compensated for than in the far field. It turns out that
an array in endfire orientation is less sensitive to a defective transit time compensation
Δ
max than an array in broad-side orientation.
[0113] In a vehicle, the average distance between the speaker, in particular, its head,
and the array is about 50cm. Due to a movement of the head, this distance can change
by about +/- 20cm. If a transit time error of 1 tap is acceptable, the distance between
the microphones in broad-side orientation with a sampling frequency of
fa = 8kHz should be smaller than about
dmic_max (broad -side)≅ 5
cm. With the same conditions, the maximum distance between the microphones in endfire
orientation may be about
dmic_max(endfire) ≅ 20
cm.
[0114] On the other hand, having a distance between the microphones of about 5cm, it turns
out that a sampling frequency of
fa = 16
kHz provides excellent results for an endfire orientation whereas in broad-side orientation,
only a sampling frequency of
fa = 8
kHz can be used without adaptive beamsteering. In other words, in endfire orientation,
the sampling frequency or the distance between the microphones can be chosen much
higher than in the broad-side case, thus, resulting in an improved beamforming.
[0115] In this context, it is to be pointed out that the larger the distance between the
microphones, the sharper the beam, in particular, for low frequencies. A sharper beam
at low frequencies increases the gain in this range which is important for vehicles
where the noise is mostly a low frequency noise.
[0116] However, the larger the microphone distance, the smaller the usable frequency range
according to the spatial sampling theorem

[0117] A violation of this sampling theorem has the consequence that at higher frequencies,
large grating lobes appear. These grating lobes, however, are very narrow and deteriorate
the gain only slightly. The maximum microphone distance that can be chosen depends
not only on the lower limiting frequency for the optimization of the directional characteristic,
but also on the number of microphones and on the distance of the microphone array
to the speaker. In general, the larger the number of microphones, the smaller their
maximum distance in order to optimize the Signal-To-Noise-Ratio (SNR). For a distance
between array and speaker of 50cm, the microphone distance, preferably, is about
dmic = 40
cm with two microphones (
M = 2) and about
dmic = 20
cm for
M = 4.
[0118] A further improvement of the directivity, and, thus, of the gain, can be achieved
by using unidirectional microphones instead of omnidirectional ones; this will be
discussed in more detail below.
[0119] Figures 5A and 5B show preferred arrangements of microphone arrays in a vehicle.
In general, the distance between the microphone array and the speaker should be as
small as possible.
[0120] According to a first embodiment (Figure 5A), each speaker 501 may have its own microphone
array comprising at least two microphones 102. The microphone arrays may be provided
at different locations, for example, within the headliner, dashboard, pillar, headrest,
steering wheel, compartment door, visor or (driving) mirror. An arrangement within
the roof is also a preferred possibility that is, however, not suitable for the case
of a cabriolet. Both microphone arrays for each speaker are in endfire orientation.
[0121] In an alternative embodiment (Figure 5B), one microphone array is used for two neighboring
speakers. In both embodiments, preferably, directional microphones, in particular,
having a cardioid characteristic, may be used.
[0122] In the embodiment of Figure 5B, the microphone array may be mounted within the mirror.
Such a linear microphone array may be used for both the driver and the front seat
passenger. A costly mounting of the microphones in the roof can be avoided. Furthermore,
the array may be mounted in one piece, which ensures a high mechanical precision.
Due to the adjustment of the mirror, the array would always be correctly oriented.
[0123] Figure 6A shows a top view on a (driving) mirror 601 of a car with three microphones
in two alternative arrangements. According to the first alternative, two microphones
602 and 603 are located in the center of the mirror in endfire orientation with respect
to the driver and, preferably, have a distance d
mic of about 5cm between each other. The microphones 603 and 604 are in endfire orientation
with respect to the front seat passenger and have a distance of about 10cm between
each other. Since the microphone 603 is used for both arrays, a cheap handsfree system
can be provided.
[0124] All three microphones may be directional microphones, preferably having a cardioid
characteristic, for example, a hypercardioid characteristic. Alternatively, microphones
602 and 604 are directional microphones, whereas microphone 603 is an omnidirectional
microphone which further reduces the costs. If all three microphones are directional
microphones, preferably, microphones 602 and 603 are directed towards the driver.
[0125] Due to the larger distance between microphones 603 and 604 than between microphones
602 and 603, the front seat passenger beamformer has a better SNR at low frequencies.
[0126] According to an alternative embodiment, the microphone array for the driver consists
of microphones 602' and 603' located at the left side of the mirror. In this case,
the distance between this microphone array and the driver would be increased, thus,
decreasing the performance. On the other hand, the distance between microphone 603'
and 604 would be about 20cm, which yields a better gain for the front seat passenger
at low frequencies.
[0127] A variant of two microphone arrays with improved precision is shown in Figure 6B.
Also in this case, all microphones may be directional microphones, microphones 602
and 603 being directed to the driver, microphones 604 and 605 being directed to a
front seat passenger. In this example, the microphone array for the front seat passenger
comprises the three microphones 603, 604 and 605, which increases the gain considerably.
[0128] It is to be noted that these arrangements are only examples that may be varied by
changing the position and number of the microphones. In particular, an arrangement
may be optimized with regard to a specific vehicular cabin.
[0129] Figure 7 illustrates a microphone array comprising three subarrays 701, 702, and
703, each subarray consisting of five microphones. Within each subarray 701, 702,
and 703, the microphones are equidistantly arranged. In the total array 704, the distances
are no longer equal. As can be seen in this figure, some microphones are used for
different arrays, therefore, for the total array, only 9 microphones and not 3.5 =
15 microphones are necessary.
[0130] In this figure, it is further indicated that the different subarrays are used for
different frequency ranges. The resulting directional diagram is then built up of
the directional diagrams of each subarray for the respective frequency range. For
the special case of Figure 7, subarray 701 with
dmic = 5
cm is used for the frequency band of 1400 - 3400 Hz, subarray 702 with
dmic = 10
cm for the frequency band of 700 - 1400 Hz, and subarray 703 with
dmic = 20
cm for the band of frequencies smaller than 700 Hz. A lower limit of this frequency
band may be imposed, for example, by the lowest frequency of the telephone band (the
frequencies used in telephone applications) which, presently, is 300 Hz in most cases.
[0131] An improved directional characteristic may be obtained if the superdirective beamformer
is designed as general sidelobe canceller (GSC). In this structure, at least one filter
can be saved. Such a superdirective beamformer in GSC structure is shown in Figure
8. The GSC structure is to be implemented in the frequency domain, thus, an FFT 201
is applied to the incoming signals
xk(
t) from microphones 102. Before the general sidelobe cancelling, a time alignment using
phase factors
ejωτk has to be performed (in this figure, a far field beamsteering is shown). If a GSC
beamformer is used in the handsfree system of Figure 1, for example, again, an inverse
Fourier transform is to be performed before passing the signal to the Wiener filters
110 or the pre-emphasis filter 111.
[0132] In Figure 8,
X denotes a vector comprising all time aligned input signals
Xi(
ω).
Ac is a vector comprising all frequency independent filter transfer functions
Ai that are necessary to observe the constraints in viewing direction;
H is the vector of the transfer functions performing the actual superdirectivity; and
B is the so-called blocking matrix projecting the input signals in
X onto the "noise plane". The signal
YDS(
ω) denotes the output signal of the delay and sum beamformer,
YBM(
ω) the resulting output signal of the blocking branch,
YSD(ω) the output signal of the superdirective beamformer
xi(
t), and
Xi (ω) the input signals in the time and frequency domain that are not yet time aligned,
and
Yi(ω) the output signals of the blocking matrix that ideally should block completely
the desired or useful signal within the input signals. The signals
Yi(ω) ideally only comprise the noise signals.
[0133] In addition to the superdirective output signal, a GSC structure also yields a delay
and sum beamformer signal and a blocking output signal. The number of filters that
can be saved using the GSC depends on the choice of the blocking matrix. Usually,
a Walsh-Hadamard blocking matrix is preferred instead of a Griffiths-Jim blocking
matrix since more filters can be saved with a Walsh-Hadamard blocking matrix. Unfortunately,
the Walsh-Hadamard blocking matrix can only be given for arrays consisting of
M =
2n microphones.
[0134] In principle, a blocking matrix should have the following properties:
1. It is a (M -1)·M -Matrix.
2. The sum of the values within one row vanishes.
3. The matrix is of rank M -1.
[0135] A Walsh-Hadamard blocking matrix for
n = 2 has the following form

[0136] According to an alternative embodiment, a blocking matrix according to Griffiths-Jim
can be used which has the general form

[0137] The upper branch of the GSC structure is a delay and sum beamformer with the transfer
functions

[0138] The computation of the filter coefficients of a superdirective beamformer in GSC
structure is slightly different compared to the conventional superdirective beamformer.
The transfer functions
Hi(
ω) are to be computed as

wherein
B is the blocking matrix and
Φnn(
ω) the matrix of the cross-correlation power spectrum of the noise. In the case of
a homogenous noise field, Φ
nn(ω) may be replaced by the time aligned coherence matrix of the diffuse noise field,
as discussed above.
[0139] A regularization and the iterative design with predetermined susceptibility may be
performed in the same way as above.
[0140] All previously discussed filter designs only assume that the noise field is homogenous
and diffuse. These designs may be generalized by excluding a region around the main
receiving direction Θ
0 when determining the homogenous noise field. In this way, mainly the Front-To-Back-Ratio
may be optimized. This is illustrated in Figure 9 with microphones 102 where a sector
of +/-δ is excluded. The computing of the two-dimensional diffuse (cylindrically isotropic),
homogenous noise field can be performed using the new design parameter δ:

[0141] This method may also be generalized to the three-dimensional case. Then, in addition
to the parameter δ being responsible for the azimuth, a further parameter ρ is to
be introduced for the elevation angle. This yields an analog equation for the coherence
of the homogeneous diffuse 3D noise field.
[0142] A superdirective beamformer based on an isotropic noise field is particularly useful
for a handsfree system which is to be installed later in a vehicle. This is the case,
for example, if the handsfree system is installed in the vehicle by the user itself.
On the other hand, an MVDR beamformer may be relevant if there are specific noise
sources at fixed relative positions or directions with respect to the position of
the microphone array. In this case, the handsfree system can be adapted to a particular
vehicular cabin by adjusting the beamformer such that its zeros point into the direction
of specific noise sources. For example, such a noise source may be formed by a loudspeaker
or a fan. Preferably, a handsfree system with MVDR beamformer is already installed
during manufacture of the vehicle.
[0143] The typical distribution of noise or noise sources in a particular vehicular cabin
can be determined by performing corresponding noise measurements under appropriate
conditions (e.g., driving noise with and/or without loudspeaker and/or fan noise).
The measured data are used for the design of the beamformer. It is to be noted that
also in this case, no further adaption is performed during operation of the handsfree
system.
[0144] Alternatively, if the relative position of a noise source is known, the corresponding
superdirective filter coefficients can also be determined theoretically.
[0145] As already stated above, the use of directional microphones further improves the
signal enhancement. Figure 10 shows a superdirective beamformer with directional microphones
1001. In this figure, each directional microphone 1001 is depicted by its equivalent
circuit diagram. In these circuit diagrams,
dDMA denotes the (virtual) distance of the two omnidirectional microphones composing the
first order pressure gradient microphone in the circuit diagram.
T is the (acoustic) delay line fixing the characteristic of the directional microphone
and
EQTP is the equalizing low path filter yielding a frequency independent transfer behavior
in viewing direction.
[0146] In practice, these circuits and filters may be realized purely mechanically by taking
an appropriate mechanical directional microphone. Again, the distance between the
directional microphones is
dmic. In Figure 10, the whole beamforming is performed in the time domain. A near field
beamsteering 104 is applied to the signals
xn[
i] coming from the microphones and being filtered by the equalizing filter
EQTP. The gain factors ν
n compensate for the amplitude differences and the delays τ
n for the transit time differences of the signals. The FIR filters α
n[
i] realize the superdirectivity in the time domain.
[0147] Mechanical pressure gradient microphones have a high quality and yield, in particular,
using a hypercardioid characteristic, an excellent array gain. The use of directional
microphones results in an excellent Front-to-Back-Ratio as well.
[0148] An example for another preferred embodiment of a handsfree system is shown in Fig.
11. The system shown in this figure differs from the system of Fig. 1 in that an adaptive
noise canceller (ANC) system 1101 is provided between the microphone array 101 and
the high-pass filters 103. The ANC system is particularly useful to reduce motor harmonics
in the signal.
[0149] The structure of an adaptive noise canceller is shown in Fig. 12. In this figure,
a wanted signal source 1201, particularly corresponding to a speaker, and a noise
source 1202 are shown. The signal entering a microphone 102 which is part of the microphone
array is the sum of a wanted signal
s[
k] and a noise signal
n0[
k]. In addition, a noise sensor 1203 is present which is to provide a pure noise signal
n1[
k]. In Fig. 12, the reference sensor 1203 is a microphone; in this case, such a microphone
should be arranged at a place where no or almost no wanted signal is to be recorded.
The output signal
y[
k] of the adaptive filter 1204 is subtracted from the output signal of the microphone
102. The input signal
n1[
k] for the adaptive filter 1204 and the output signal or error signal
e[
k] serve for adaption of the noise canceller. The noise reduction of the adaptive noise
canceller depends only on the coherence of the signals of the microphone 102 and the
reference sensor 1204; this coherence function in turn is depending on the distance
between microphone and reference sensor.
[0150] According to a preferred alternative, the reference sensor is not an acoustic sensor.
One possibility is to couple an electrical sensor with the tachometer or speed counter
which is usually present in a vehicle. After determining the interrelationship between
the tachometer signal and the motor noise, the latter may be subtracted from the microphone
signal via the adaptive noise canceller. Such an embodiment is shown in Fig. 11 where
a tachometer 1102 is coupled to the ANC 1101.
[0151] The ANC need not be placed directly behind the microphone array 101. According to
a preferred alternative, the ANC may be used to filter the output signal x[k] of the
superdirective beamformer. In this case, the ANC is to be placed between the summing
circuit 107 and the adaptive post-filter 108.
[0152] A further noise reduction with the help of an ANC system can be achieved by using
additional - acoustical or non-acoustical - noise sensors. A corresponding embodiment
is shown in Fig. 13. In this embodiment, the ANC system 1304 is used particularly
to suppress signals coming from a loudspeaker 1301, for example, emitting a far end
signal 1302. The ANC system is able to create a so-called area of silence around the
noise sensor or noise sensors. If the microphone array 101 is located in the vicinity
of the near speaker, the whole array 101 or one of its microphones 102 may be used
as noise sensor. Alternatively, one or more acoustical (1203) and/or non-acoustical
(1102) noise sensors are to be installed. Ideally, an acoustical ANC can provide a
noise reduction for both the near and far end speaker.
[0153] As can be seen in Fig. 13, an additional acoustic echo canceller (AEC) system 1303
may also be provided. Such an AEC system is optional, but allows a suppression of
reverberation. Preferably, each microphone 102 is provided with an individual AEC
filter. Alternatively, an AEC filter may be placed between the summing circuit and
the adaptive post-filter. In such an alternative embodiment, an ANC may be placed
between the summing circuit and the AEC.
[0154] Advantageously, the AEC system used for this invention comprises a conventional AEC
filter and an integrated frequency selected echo attenuation which acts as a residual
echo suppression (RES) algorithm. A preferred embodiment of such a system is shown
in Fig. 14. It comprises a conventional AEC filter 1303 that filters the far end signal
1302. The adaption of the conventional AEC filter 1303 is performed using the signal
1401, e.g. the output signal of an electrical ANC or of one of the microphones. Furthermore,
an echo shaping means 1402 is provided. This echo shaping means has the form of an
adaptive FIR filter with coefficient vector
H[
k] that filters the compensated signal
e[
k]. The coefficient vector
H[
k] of the adaptive filter is taken in each sampling step from another adaptive FIR
filter with coefficient vector
H1[
k]. Preferably, the filter with coefficient vector
H1[
k] is a linear-phase filter of low order. The echo shaping means further comprises
a delay element
TH1. The adaption algorithm is based on the power difference between
y[
k] and
e[
k] by filtering according to

wherein the compensated signal
e[
k] is the reference signal. The resulting signal
z[
k] depends on the time varying factor α[
k]. In case the far end speaker is active, the output signal
y[
k] of the ANC system is dominant (
α[
k] close to 1) at the input of the adaptive filter with coefficient vector
H1[
k]. Then, the adaptive filter with coefficient vector
H1[
k] can reduce the error signal

[
k] only by suppressing the signal of the far end speaker. In this case, the near speaker
and local noise signals will not be attenuated by the echo shaping means. It is to
be noted that the echo shaping algorithm is frequency selective.
[0155] The system shown in Fig. 15 is able to process speech signals from two different
positions (for example, from the driver and the front seat passenger in a car). The
microphone array 101 has a directional diagram with two preferred directions. For
example, directional microphones may be used and/or the microphones may be arranged
in a suitable way. One or several ANC or AEC filters can provide an estimation of
the noise level present in the microphone signals that may be used in the dynamic
volume control (DVC) 1501 to vary the volume of the far end speech signal 1511 in
dependence of the noise level. The system comprises a beamformer for both wanted signal
sources each comprising a beamsteering means 1502 and 1504 and beamformer filters
1503 and 1505. Following each beamformer, adaptive post-filters 1506 and 1507 are
arranged which, in turn, are directly connected to non-adaptive post-filters 1508
and 1509.
[0156] The output signals of the non-adaptive post-filters are fed to a unit 1510 comprising
two voice activity detectors and a switch control that generates a weighting factor
A for combining both signals. According to a possible example for such a unit 1510,
each of the signals
s
[
k] and
s
[
k] can be processed by a low-pass filter. Then, for each of the filtered signals, the
contained noise signal or its level is estimated using, for example, a minimum statistics.
The noise signal level is subtracted from the corresponding filtered signal level.
The resulting signal levels are compared to a threshold value. Depending on this comparison
of both signal levels, the weighting factor
A is determined. For example, a possible weighting can be determined as follows. If
both signal levels are larger than the threshold value, both signals are equally weighted.
If one of the signal levels is larger than the threshold value and the other is smaller
than the threshold value, the larger signal is weighted by a factor of 1 and the other
is fully suppressed (weighting factor 0). If both signal levels are smaller than the
threshold value, the signal stemming from the direction of the driver's seat is weighted
by a factor of 1 and the other signal is fully suppressed.
[0157] The combined signal may be subject to an additional post processing 1512, for example,
a residual echo suppression (RES). For a RES, the combined signal is weighted by a
spectral short time gain in the frequency domain, wherein the gain factor depends
on the spectrum of the far end speech signal.
1. Handsfree system for use in a vehicle comprising a microphone array (101) with at
least two microphones (102), a signal processing means, and an adaptive post-filter
(108), the signal processing means comprising a beamformer having an input connected
to the at least two microphones and an output connected to the input of the adaptive
post-filter.
2. Handsfree system according to claim 1, wherein the adaptive post-filter is a filter
in the time domain.
3. Handsfree system according to claim 1 or 2, wherein the adaptive post-filter is a
Wiener filter and/or a linear-phase filter.
4. Handsfree system according to one of the preceding claims, wherein the signal processing
means further comprises at least two adaptive filters (110) having an input connected
to the output of the beamsteering means (104) and an output connected to the adaptive
post-filter, wherein the at least two adaptive filters are configured to determine
adaptive filter parameters for the adaptive post-filter.
5. Handsfree system according to claim 4, wherein for each of the at least two microphones,
an adaptive filter is provided having an input connected to the output of the beamsteering
means.
6. Handsfree system according to claim 4 or 5, wherein an input of each of the at least
two adaptive filters is further connected to the output of the beamformer.
7. Handsfree system according to one of the preceding claims, wherein the signal processing
means further comprises a pre-emphasis filter (111), in particular, comprising a pre-whitening
filter, having an input connected to an output of the adaptive post-filter and/or
a pre-emphasis filter, in particular, comprising a pre-whitening filter, having an
input connected to the output of the beamsteering means and an output connected to
the at least two adaptive filters.
8. Handsfree system according to one of the preceding claims, wherein the signal processing
means further comprises an inverse filter (106), particularly a warped inverse filter.
9. Handsfree system according to one of the preceding claims, wherein the signal processing
means further comprises a non-adaptive post-filter (109) having an input connected
to an output of the adaptive post-filter.
10. Handsfree system according to one of the preceding claims, wherein the signal processing
means further comprises an adaptive noise canceller (ANC) (1101) and/or an acoustic
echo canceller (AEC) (1303).
11. Handsfree system according to one of the preceding claims, wherein the beamformer
is a non-adaptive beamformer and/or a superdirective beamformer and/or a minimum variance
distortionless response (MVDR) beamformer.
12. Handsfree system according to one of the preceding claims, wherein the microphone
array comprises at least two microphones being arranged in endfire orientation with
respect to a first position.
13. Handsfree system according to claim 12, wherein the microphone array comprises at
least two microphones being arranged in endfire orientation with respect to a second
position.
14. Handsfree system according to claim 13, wherein the at least two microphones in the
first endfire orientation and the at least two microphones in the second endfire orientation
have a microphone in common.
15. Handsfree system according to one of the preceding claims, wherein the signal processing
means comprises at least two beamformers.
16. Handsfree system according to claim 15, further comprising a voice activity detector
(VAD) and/or a switch control means (1510).
17. Handsfree system according to one of the preceding claims, further comprising a residual
echo suppression (RES) means and/or a dynamic volume control (DVC) (1510).
18. Handsfree system according to one of the preceding claims, wherein the microphone
array comprises at least two subarrays (701, 702, 703).
19. Handsfree system according to one of the preceding claims, further comprising a frame
wherein each microphone of the microphone array is arranged in a predetermined, in
particular fixed, position in or on the frame.
20. Handsfree system according to one of the preceding claims, wherein at least one microphone
is a directional microphone (1001), in particular, having a cardioid characteristic
and/or being a differential microphone.
21. Vehicle comprising a handsfree system according to one of the preceding claims.
22. Method for noise reduction in a vehicular handsfree system, comprising receiving input
signals resulting from a microphone array with at least two microphones, processing
the input signals by a beamformer to provide a beamformed signal, and adaptively filtering
a signal resulting from the beamformed signal by an adaptive post-filter.
23. Method according to claim 22, wherein the adaptive filtering is performed in the time
domain.
24. Method according to claim 22 or 23, further comprising
providing at least two adaptive filters, particularly Wiener filters, wherein
processing the input signals by a beamformer comprises beamsteering the input signals
for providing beamsteered signals corresponding to one of the at least two microphones
and summing the signals, and
adaptively filtering comprises receiving and processing at least one beamsteered signal
by at least one of the at least two adaptive filters to determine adaptive filter
parameters for the adaptive post filter.
25. Method according to claim 24, wherein adaptively filtering further comprises receiving
a signal resulting from the beamformed signal by at least one adaptive filter and
wherein processing the beamsteered signal comprises determining adaptive filter parameters
using the at least one beamsteered signal and the signal resulting from the beamformed
signal.
26. Method according to claim 24 or 25, wherein for each beamsteered signal, an adaptive
filter is provided for determining adaptive filter parameters using the beamsteered
signal and the signal resulting from the beamformed signal.
27. Method according to one of the claims 24 - 26, wherein receiving at least one beamsteered
signal by at least one of the at least two adaptive filters comprises processing the
at least one beamsteered signal by a pre-emphasis filter, in particular, comprising
a pre-whitening filter.
28. Method according to one of the claims 22 - 27, further comprising processing a signal
resulting from the microphone array by an inverse filter, in particular, a warped
inverse filter.
29. Method according to one of the claims 22 - 28, further comprising non-adaptively filtering
a signal resulting from the adaptively filtered signal and/or processing a signal
resulting from the adaptively filtered signal by a pre-emphasis filter.
30. Method according to one of the claims 22 - 29, further comprising processing a signal
resulting from the microphone array, particularly resulting from the beamformed signal,
by an adaptive noise canceller (ANC) and/or an acoustic echo canceller (AEC) and/or
a residual echo suppression (RES) means.
31. Method according to one of the claims 22 - 30, wherein the input signals are processed
by a non-adaptive and/or superdirective and/or minimum variance distortionless response
(MVDR) beamformer.
32. Computer program product comprising one or more computer readable media having computer-executable
instructions for performing the steps of the method according to claims 22 - 31.