Adaptive de-reverberation method based on Kalman filter

Abstract

The invention discloses an adaptive de-reverberation method based on Kalman filter. The method comprises the following steps: (1) using a microphone to obtain a signal, and then converting the analog signal into a digital signal through AD conversion; (2) after performing short-time Fourier transform on the signal, using Kalman At the same time, the sound source localization method is used to judge whether the sound source position changes. If the sound source position changes, the state prediction error vector covariance matrix of the Kalman filter is reset to the initial value; (3) The time-frequency domain anti-reverberation signal output by the Kalman filter in step 2 is subjected to short-time Fourier transform to obtain a time-domain anti-reverberation signal. The method of the present invention can effectively improve the reverberation performance of the Kalman filter in the scene of a sudden change in the sound source.

Description

technical field [0001] The invention belongs to the field of speech enhancement, and in particular relates to an adaptive de-reverberation method based on Kalman filtering. Background technique [0002] When the microphone is used to collect the speaker's voice signal in the room, the reflected sound from the wall of the room will be collected at the same time. These reflected sounds are called reverberation. When the reverberation time is long, the reverberation will affect the intelligibility of speech in voice communication, and will also reduce the recognition rate of the speech recognition system. [0003] Spectral subtraction can be used to implement speech reverberation (Lebart K, Boucher J M, Denbigh P N. A New Method Based on Spectral Subtraction for Speech Dereverberation [J]. ActaAcustica United with Acustica, 2001, 87(3): 359-366.). In the Chinese patent CN102750956A, the short-time Fourier transform is used to transform the single-channel speech signal into the...

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Problems solved by technology

However, since the identity matrix is ​​used as the state transition matrix in this paper, the performance of the Kalman filter drops significantly after a sudden change in the position of the sound source

Literature (T.Dietzen, S.Doclo, A.Spriet, W.Tirry, M.Moonen, and T.van Waterschoot, "Low-Complexity Kalman filter for multi-channel linear-prediction-based blind speechdereverberation," in 2017IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2017, pp.284–288.) used a constant multiplied by a unit matrix less than 1 as the state transition matrix, and there was no significant difference in algorithm performance before and after the sound source position was changed, but The overall performance of the algorithm is poor

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