초록 ▼

A novel enhanced beamforming technique that improves beamforming operations by incorporating a model for the directional gains of the sensors, such as microphones, and provides means of estimating these gains. The technique forms estimates of the relative magnitude responses of the sensors (e.g., mi

A novel enhanced beamforming technique that improves beamforming operations by incorporating a model for the directional gains of the sensors, such as microphones, and provides means of estimating these gains. The technique forms estimates of the relative magnitude responses of the sensors (e.g., microphones) based on the data received at the array and includes those in the beamforming computations.

대표청구항 ▼

1. A computer-implemented process for improving the signal to noise ratio of one or more signals from sensors of a sensor array, comprising: inputting signals of sensors of a sensor array in the frequency domain defined by frequency bins;for each frequency bin, computing a beamformer output as a fun

1. A computer-implemented process for improving the signal to noise ratio of one or more signals from sensors of a sensor array, comprising: inputting signals of sensors of a sensor array in the frequency domain defined by frequency bins;for each frequency bin, computing a beamformer output as a function of weights for each sensor, wherein the weights are computed using combined noise from reflected paths and auxiliary sources, and a sensor array response which includes the intrinsic gain of each sensor as well as its directional propagation loss from the source to the sensor;combining the beamformer outputs for each frequency bin to produce an output signal with an increased signal to noise ratio over what would be obtainable directional gain of each sensor and its directional propagation loss into account. 2. The computer-implemented process of claim 1 wherein the input signals of the sensor array in the frequency domain are converted from the time domain into the frequency domain prior to inputting them using a Modulated Complex Lapped Transform (MCLT). 3. The computer-implemented process of claim 1 wherein the sensors are microphones and wherein the sensor array is a microphone array. 4. The computer-implemented process of claim 1 wherein the sensors are one of: sonar receivers and wherein the sensor array is a sonar array;directional radio antennas and the sensor array is a directional radio antenna array; andradars and wherein the sensor array is a radar array. 5. The computer-implemented process of claim 1 wherein computing a beamformer comprises employing a minimum variance distortionless response beamformer. 6. The computer-implemented process of claim 1 wherein computing the beamformer output comprises: computing an estimate of the relative gain of each sensor;computing an array response vector, using the computed relative gains of each sensor and the time delay of propagation between the source and each sensor;using the array response vector and a combined noise covariance matrix representing noise from reflected paths and auxiliary sources to obtain a weight vector; andcomputing an enhanced output signal by multiplying the weight vector by the input signals. 7. The computer-implemented process of claim 6 wherein the signal time delay of propagation is computed using a sound source localization procedure. 8. The computer-implemented process of claim 6 wherein the combined noise matrix is obtained by using a voice activity detector. 9. A computer-implemented process for improving the signal to noise ratio of one or more signals from sensors of a sensor array, comprising: inputting signal frames from microphones of a microphone array in the frequency domain;inputting each frame in the frequency domain into a voice activity detector which classifies the frame as speech, noise or not sure;if the voice activity detector identifies the frame as speech, computing the direction of arrival of the source signal using sound source localization and using the direction of arrival to update an estimate of the source location;if the voice activity detector identifies the frame as noise, computing a noise estimate and using it to update a combined noise covariance matrix representing reflected sound and sound from auxiliary sources;computing a beamformer output using the frames classified as Speech, Not Sure or as Noise, the sound source location, the noise covariance matrix, and an array response vector which includes the relative gains of the sensors, to produce an output signal with an enhanced signal to noise ratio. 10. The computer-implemented process of claim 9 wherein computing the beamformer output comprises: computing an estimate of the relative gain of each sensor;computing an array response vector, using the computed relative gains and the time delay of propagation between the source and each sensor;using the array response vector and a combined noise covariance matrix representing noise from reflected paths and auxiliary sources to obtain a weight vector; andcomputing an enhanced output signal by multiplying the weight vector by the input signals. 11. The computer-implemented process of claim 9 further comprising converting the output signal from the frequency domain to the time domain. 12. The computer-implemented process of claim 9 wherein the voice activity detector evaluates all frequency bins of the frame in identifying the frame as speech. 13. A system for improving the signal to noise ratio of a signal received from a microphone array, comprising: a general purpose computing device;a computer program comprising program modules executable by the general purpose computing device, wherein the computing device is directed by the program modules of the computer program to,capture audio signals in the time domain with a microphone array;convert the time-domain signals to the frequency-domain using a converter;input the frequency domain signals divided into frames into a Voice Activity Detector (VAD), that classifies each signal frame as either Speech, Noise, or Not Sure; if the VAD classifies the frame as Speech, perform sound source localization in order to obtain a better estimate of the location of the sound source which is used in computing the time delay of propagation;if the VAD classifies the frame as Noise the signal is used to update a noise covariance matrix, which provides a better estimate of which part of the signal is noise; andperform beamforming using the frames classified as Speech, Not Sure or as Noise, the noise covariance matrix, the sound source location, and an array response vector which includes an estimate of the relative gains of the sensors, to produce an enhanced output signal in the frequency domain. 14. The system of claim 13 wherein the VAD uses more than one frequency bin of the frame to classify the input signal as noise. 15. The system of claim 14 wherein the noise covariance matrix is computed from frames classified as noise by computing their sample mean. 16. The system of claim 13 further comprising at least one module to: encode the enhanced beamformer output;transmit the encoded enhanced beamformer output; andtransmit the enhanced beamformer output. 17. The system of claim 13 wherein the beamforming module comprises sub-modules to: compute an estimate of the relative gain of each sensor;use the sound source location and the estimated relative gains to compute the array response vector;use the noise covariance matrix and the computed array response vector, to compute a weight vector; andcompute the enhanced output signal by multiplying the weight vector by the input signal. 18. The system of claim 13 wherein the beamformer output is computed using a minimum variance distortionless response beamformer. 19. The system of claim 13 wherein the microphones of the microphone array are arranged in a circular configuration. 20. The system of claim 13 wherein the microphones of the array are directional.