Electrical audio signal processing systems and devices – Directive circuits for microphones
Reexamination Certificate
1999-10-25
2003-07-15
Harvey, Minsun Oh (Department: 2644)
Electrical audio signal processing systems and devices
Directive circuits for microphones
C367S119000
Reexamination Certificate
active
06594367
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to signal processing, and more particularly, to processing the signals received by an array of sensors in order to minimize the amount of noise received by the array when the array is being used to receive a desired signal.
BACKGROUND OF THE INVENTION
Beamforming is a term used to designate the operations associated with forming spatial sensitivity pattern for an array of sensors. Classical beamforming is defined as “delay and sum beamforming”. In delay and sum beamforming, a source transmits a wave that propagates and arrives at an array of sensors at different times, depending on the source direction and the array geometry. The outputs of the sensors of the array are delayed, to compensate for the delay in time of arrival of the source's wave, which originated from the preferred direction, and summed, to provide a classical directional beamformer output. The effect of sources that are located at directions other than the preferred direction (referred to as the looking direction) is reduced by the beamforming process, resulting in maximum sensitivity of the process towards the preferred direction.
The array of sensors can be, for example, an array of microphones receiving an acoustic sound source. The beamforming process can be used to map sound sources (in a sonar system for example), or to emphasize a sound source whose direction is known, by modifying the compensating delays and “steering” the look direction of the array. The beam-width—usually defined as the difference between the two angles, in which the output energy is reduced by 3 dB relative to the beam center—depends on the array length, frequency of the received signal and propagation speed of the received signal (in our example the speed of sound). For many practical purposes the beam-width of the array will not be sufficiently narrow, and enlarging the array length is not desired. For those cases a more directional beamforming process is required.
Moreover, while delay and sum beamforming, does not provide optimum noise reduction. If the sensors' outputs are filtered (a different filter to each sensor) and the outputs of the filters summed, one can obtain a different shape of the beamformer output and improve noise reduction. With a careful design it is possible, for example, to create a null (zero reception) towards a given direction. If a noise source's direction is known and a null is placed in that direction, improved noise reduction can be realized as compared to the noise reduction of the classic delay and sum beamformer.
Two basic approaches have been developed to obtain optimum performance of a beamformer in the presence of noise. The first one, presented in Monzingo and Miller—Introduction to Adaptive Arrays (Wiley, N.Y.) pp. 89-105 and 155-216 shows that if a filter is created for each sensor that for each frequency will have gain weights of
w
opt
=
C
-
1
v
v
C
-
1
v
(
1
)
the output of the beamformer will have optimum performance in terms of noise reduction. The above weights will maintain a unity gain at the look direction (no distortion of the desired signal) while providing minimum energy at the output. The two assumptions (minimum energy and no signal degradation) will result minimum noise at the output. In Eq. (1) C is the noise covariance matrix and it may be expressed as:
C=E{y*y
T
} (2) where
y
T
=[y
1
(
f
)
y
2
(
f
) . . .
y
n
(
f
)] (3)
is the noise measurement at the elements, and v is the steering vector towards the look direction, expressed as:
v
=
[
ⅇ
-
j
wr0
ⅇ
-
j
wr1
ⅇ
-
j
wr
(
n
-
1
)
]
where
(
4
)
&tgr;
0
−&tgr;(n−1) are the steering delays introduced to elements 0−n respectively by a target originated at the look direction. Further, the filtered elements approach was extended by Frost (O. L. Frost, III, “An Algorithm for Linearly Constrained Adaptive Array Processing,”
Proc. IEEE, vol
. 60, no. 8, pp. 926-935, August 1972.) to provide an adaptive beamformer in which the weights would adapt themselves so that they converge to provide the optimum solution.
The second basic approach to obtain optimum beamformer performance was developed by Griffiths (L. J. Griffiths and C. W. Jim, “An Alternative Approach to Linearly Constrained Adaptive Beamforming,”
IEEE Trans. Antennas Propagat
., vol. AP-30, no. 1, pp. 27-34, January 1982.) who suggested using a Noise Canceling (NC) approach to the optimum beamformer problem. In his approach the adaptive coefficient are updated by the Least Mean Squares (LMS) algorithm. Griffiths proposed using the elements' signals to obtain a main channel, in which both the signal and the noise are present, and reference channels, in which only noise is present (i.e. which are signal free). The main channel can be generated through one of the elements alone, or through classic delay and sum beamforming. The reference channels can be generated through the subtraction of one element from another, or by forming any other linear combination of elements that would provide a zero output at the look direction (i.e. the signal direction). The main channel and the reference channels are utilized by an adaptive LMS Widrow filter to obtain an optimum beamformer (see Adaptive Noise Canceling: Principals and Applications—Widrow, Glover, McCool—
Proc. IEEE
vol. 63 no. 12 1692-1716, December 1975). In this adaptive beamformer each reference channel is filtered (i.e. each channel signal is convolved with a set of filter coefficients), the filtered channels are summed together to obtain the noise estimation, and the noise estimation is subtracted from the main channel to provide a noise free signal. The filter coefficients in the Griffiths solution converge to
w
opt
=C
−1
p
(5) where
C is the noise covariance matrix and p is the correlation vector between the beam output and the reference channels. Note that with this approach the steering is done through the creation of the reference channels and the beam, so there is no steering vector towards the look direction in equation (5). Griffiths showed that, for an n elements system, if one creates n−1 reference channels, the LMS approach would converge to the same optimum solution as Frost.
Objects and Summary of the Invention
It has been recognized that while the two approaches to optimum beamforming discussed above were primarily developed to provide an adaptive solution, they also teach us what the optimum solution would be given the noise covariance matrix. A non-adaptive approach, in which predetermined filters are designed and used, is sometimes more appealing than an adaptive approach. The fixed beam (non-adaptive) approach is much less computationally intensive, it is much less sensitive to leakage of the desired signal to the reference channels and it does not give rise to distortion in the desired signal. Also, the fixed approach has the potential to handle some types of noises better than an adaptive process, such as reverberation and diffused low noises. On the other hand, one may not want to give up the adaptive process, because it provides the best immunity to significant directional noises. A hybrid system that uses both adaptive and non-adaptive techniques provides a system which realizes the advantages of both techniques.
Further, it has been recognized that while the above described optimum beamforming techniques provide the solution given the noise covariance matrix, they do not show how to determine this matrix for a particular noise scenario. Also, the equations show how the required weights for each frequency can be computed, but they do not show how to implement the time domain filters that will approximate the weighting solution. The prior work in this area does not discuss how such time domain filters would be designed or implemented in a combined adaptive
on-adaptive beamforming system. Moreover, there is no teaching as to tec
Berdugo Baruch
Marash Joseph
Andrea Electronics Corporation
Frommer & Lawrence & Haug LLP
Grier Laura A.
Harvey Minsun Oh
Kowalski Thomas J.
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