Voice noise reduction method and device based on signal autocorrelation

Abstract

The invention relates to a voice noise reduction method and device based on signal autocorrelation. The method comprises steps that 1), a noise-containing simulation voice signal is acquired through avoice reception module; 2), the simulation signal is converted by an A/D conversion module into a digital signal; 3), the digital signal is enframed through a voice enframing module; 4), autocorrelation operation of the signals after enframing is carried out through an autocorrelation de-noising module; 5), FFT analysis on an autocorrelation function of each noise-containing signal in a time-delay domain is carried out through an FFT analysis module; 6), threshold de-noising processing for FFT transformation is carried out through a threshold noise reduction module; 7), re-sampling of a frequency domain autocorrelation signal is carried out through a re-sampling and phase matching module, and phase matching of the frequency domain autocorrelation signal and the noise-containing signals iscarried out; 8), inverse FFT transformation is carried out by an inverse FFT transformation module through utilizing a frequency domain amplitude after processing and the phase, and the signals are returned to the time domain; and 9), recombination of each signal after de-noising is carried out through a frame recombination module to acquire voice signals after de-noising. The method is advantaged in that language sharpness can be substantially improved under the -20dB signal to noise ratio strong noise environment.

Description

Technical field [0001] The invention relates to a noise reduction technology in the field of speech recognition, in particular to a method and device for speech noise reduction based on signal autocorrelation. Background technique [0002] Voice noise reduction is mainly used in speech recognition in human-machine conversations, and in the field of voice communication (including traditional wired communication, wireless communication and network communication). Voice noise greatly reduces the ability of smart devices and human ears to correctly recognize voice, while voice noise reduction enhances the recognition rate of voice in a noisy environment. Therefore, it is also necessary to use voice noise reduction technology in human-machine dialogue and communication equipment. It can enhance the accuracy of smart devices to recognize and execute voice commands, reduce human fatigue caused by distinguishing noisy voices, and improve voice reliability. Intelligibility. [0003] The t...

AI Technical Summary

Problems solved by technology

However, due to the complexity and non-stationarity of the speech signal, these traditional noise reduction methods are difficult to reduce the noise and effectively identify the signal with a signal-to-noise ratio lower than 10dB, especially for broadband noise, transient noise, and strong noise. Inhibition effect is not satisfactory

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