Beam forming method and system based on time-frequency masking value estimation

Abstract

The invention belongs to the technical field of speech enhancement, and particularly relates to a beam forming method and system based on time-frequency masking value estimation, and the method comprises the steps: obtaining a multi-channel speech sequence, and extracting amplitude spectrum features and spatial domain features through Fourier transform; carrying out logarithm transformation on the amplitude spectrum characteristics to obtain a multi-channel voice frequency spectrum characteristic sequence, and sending the multi-channel voice frequency spectrum characteristic sequence to a pre-trained and optimized neural network model to obtain a complex value time-frequency masking value; converting the complex value time-frequency masking value into a voice existence probability, and obtaining a time-frequency masking value by utilizing a probability model; calculating a voice signal covariance matrix according to the time-frequency masking value and the multi-channel voice feature sequence, and performing eigenvalue decomposition on the covariance matrix to obtain a beamforming filter coefficient; and in combination with the beamforming filter coefficient, performing filtering processing on the multi-channel voice sequence voice features by using a beamforming filter to obtain an enhanced voice signal. According to the invention, the neural network and spatial clustering are integrated to estimate the time-frequency masking value, and the performance of beam forming and speech recognition is improved.

Description

technical field [0001] The invention belongs to the technical field of speech enhancement, in particular to a beamforming method and system based on time-frequency masking value estimation. Background technique [0002] Speech coding and speech recognition research is often performed under laboratory conditions, that is, in environments with high signal-to-noise ratios or in noise-free environments. Therefore, when speech processing moves from the laboratory to practical application, many methods cannot be used due to the existence of actual environmental noise and interference, and the performance will drop rapidly. Therefore, it is a practical problem that must be solved to study the processing of improving the auditory effect or improving the signal-to-noise ratio of the noise-reduced speech. The essence of speech enhancement is speech noise reduction. In other words, in daily life, the speech collected by the microphone is usually "polluted" speech with different noises...

AI Technical Summary

Problems solved by technology

In the existing speech enhancement processing, the time-frequency masking value estimation based on the neural network has the problem of training-test data mismatch, which affects the performance. The time-frequency masking value estimation based on the spatial domain clustering has the problem of long iteration time. The value masking value uses the amplitude information of the feature, but does not make full use of the phase information in the feature. The estimation accuracy of the probability of speech and noise needs to be improved.

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