Electrical audio signal processing systems and devices – Directive circuits for microphones
Reexamination Certificate
1998-08-06
2002-11-19
Isen, Forester W. (Department: 2644)
Electrical audio signal processing systems and devices
Directive circuits for microphones
Reexamination Certificate
active
06483923
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to signal processing, and more specifically to an adaptive signal processing system and method for reducing interference in a received signal.
There are many instances where it is desirable to have a sensor capable of receiving an information signal from a particular signal source where the environment includes sources of interference signals at locations different from that of the signal source. One such instance is the use of microphones to record a particular party's speech in a room where there are other parties speaking simultaneously, causing interference in the received signals.
If one knows the exact characteristics of the interference, one can use a fixed-weight filter to suppress it. But it is often difficult to predict the exact characteristics of the interference because they may vary according to changes in the interference sources, the background noise, acoustic environment, orientation of the sensor with respect to the signal source, the transmission paths from the signal source to the sensor, and many other factors. Therefore, in order to suppress such interference, an adaptive system that can change its own parameters in response to a changing environment is needed.
An adaptive filter is an adaptive system that can change its own filtering characteristics in order to produce a desired response. Typically, the filter weights defining the. characteristics of an adaptive filter are continuously updated so that the difference between a signal representing a desired response and an output signal of the adaptive filter is minimized.
The use of adaptive filters for reducing interference in a received signal has been known in the art as adaptive noise cancelling. It is based on the idea of cancelling a noise component of a received signal from the direction of a signal source by sampling the noise independently of the source signal and modifying the sampled noise to approximate the noise component in the received signal using an adaptive filter. For a seminal article on adaptive noise cancelling, see B. Widrow et al., Adaptive Noise Cancelling: Principles and Applications, Proc. IEEE 63:1692-1716, 1975.
A basic configuration for adaptive noise cancelling has a primary input received by a microphone directed to a desired signal source and a reference input received independently by another microphone directed to a noise source. The primary input contains both a source signal component originating from the signal source and a noise component originating from the noise source. The noise component is different from the reference input representing the noise source itself because the noise signal must travel from the noise source to the signal source in order to be included as the noise component.
The noise component, however, is likely to have some correlation with the reference input because both of them originate from the same noise source. Thus, a filter can be used to filter the reference input to generate a cancelling signal approximating the noise component. The adaptive filter does this dynamically by generating an output signal which is the difference between the primary input and the cancelling signal, and by adjusting its filter weights to minimize the mean-square value of the output signal. When the filter weights settle, the output signal effectively replicates the source signal substantially free of the noise component because the cancelling signal closely tracks the noise component.
Adaptive noise cancelling can be combined with beamforming, a known technique of using an array of sensors to improve reception of signals coming from a specific direction. A beamformer is a spatial filter that generates a single channel from multiple channels received through multiple sensors by filtering the individual multiple channels and combining them in such a way as to extract signals coming from a specific direction. Thus, a beamformer can change the direction of receiving sensitivity without physically moving the array of sensors. For details on beamforming, see B. D. Van Veen and K. M. Buckley, Beamforming: Versatile Approach to Spatial Filtering, IEEE ASSP Mag. 5(2), 4-24.
Since the beamformer can effectively be pointed in many directions without physically moving its sensors, the beamformer can be combined with adaptive noise cancelling to form an adaptive beamformer that can suppress specific directional interference rather than general background noise. The beamformer can provide the primary input by spatially filtering input signals from an array of sensors so that its output represents a signal received in the direction of a signal source. Similarly, the beamformer can provide the reference input by spatially filtering the sensor signals so that the output represents a signal received in the direction of interference sources. For a seminal article on adaptive beamformers, see L. J. Griffiths & C. W. Jim, An Alternative Approach to Linearly Constrained Adaptive Beamforming, IEEE Trans. Ant. Prop. AP-30:27-34, 1982.
One problem with a conventional adaptive beamformer is that its output characteristics change depending on input frequencies and sensor directions with respect to interference sources. This is due to the sensitivity of a beamformer to different input frequencies and sensor directions. A uniform output behavior of a system over all input frequencies of interest and over all sensor directions is clearly desirable in a directional microphone system where faithful reproduction of a sound signal is required regardless of where the microphones are located.
Another problem with adaptive beamforming is “signal leakage”. Adaptive noise cancelling is based on an assumption that the reference input representing noise sources is uncorrelated with the source signal component in the primary input, meaning that the reference input should not contain the source signal. But this “signal free” reference input assumption is violated in any real environment. Any mismatch in the microphones (amplitude or phase) or their related analog front end, any reverberation caused by the surroundings or a mechanical structure, and even any mechanical coupling in the physical microphone structure will likely cause “signal leakage” from the signal source into the reference input. If there is any correlation between the reference input and the source signal component in the primary input, the adaptation process by the adaptive filter causes cancellation of the source signal component, resulting in distortion and degradation in performance.
It is also important to confine the adaptation process to the case where there is at least some directional interference to be eliminated. Since nondirectional noise, such as wind noise or vibration noise induced by the mechanical structure of the system, is typically uncorrelated with the noise component of the received signal, the adaptive filter cannot generate a cancelling signal approximating the noise component.
Prior art suggests inhibiting the adaptation process of an adaptive filter when the signal-to-noise ratio (SNR) is high based on the observation that a strong source signal tends to leak into the reference input. For example, U.S. Pat. No. 4,956,867 describes the use of cross-correlation between two sensors to inhibit the adaptation process when the SNR is high.
But the prior art approach fails to consider the effect of directional interference because the SNR-based approach considers only nondirectional noise. Since nondirectional. noise is not correlated to the noise component of the received signal, the adaptation process searches in vain for new filter weights, which often results in cancelling the source signal component of the received signal.
The prior art approach also fails to consider signal leakage when the source signal is of a narrow bandwidth. In a directional microphone application, the source signal often contains a narrow band signal, such as speech signal, with its power spectral density concentrated in a narrow frequency range. When signal leakage
Andrea Electronics Corporation
Frommer Lawrence & Haug
Isen Forester W.
Kowalski Thomas J.
McChesney Elizabeth
LandOfFree
System and method for adaptive interference cancelling does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with System and method for adaptive interference cancelling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and System and method for adaptive interference cancelling will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2934615