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Speech enhancement method based on Gaussian mixture model (GMM) noise estimation

A technology of noise estimation and speech enhancement, applied in speech analysis, speech recognition, instruments, etc., can solve problems such as inability to track non-stationary noise in real time and delay in noise estimation

Inactive Publication Date: 2015-03-25
HOHAI UNIV
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Problems solved by technology

The main disadvantage of this method is that there is a certain delay in noise estimation, and it cannot track non-stationary noise in real time

Method used

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  • Speech enhancement method based on Gaussian mixture model (GMM) noise estimation
  • Speech enhancement method based on Gaussian mixture model (GMM) noise estimation
  • Speech enhancement method based on Gaussian mixture model (GMM) noise estimation

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Embodiment Construction

[0020] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0021] Such as figure 1 As shown, the speech enhancement method based on GMM noise estimation mainly includes modules such as noise estimation, spectral subtraction coefficient estimation, speech frame spectral subtraction, and non-speech frame processing. The specific implementation of each module in the drawings will be described in detail below one by one.

[0022] 1. Speech preprocessing:

[0023] Preprocess noisy speech, including windowing, framing, and FFT. The window function generally ...

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Abstract

The invention discloses a speech enhancement method based on Gaussian mixture model (GMM) noise estimation, wherein the GMM is used for estimating background noise and a spectral subtraction coefficient, spectral subtraction is conducted on noisy speech, and pure speech is recovered. Firstly, the noisy speech is preprocessed so as to obtain the amplitude and phase of the noisy speech, the amplitude is used for noise estimation and spectral subtraction, and the phase is used for recovering a time-domain signal; then, the GMM is used for estimating noise parameters and pure speech cepstrum characteristics from the noisy speech in real time, and the spectral subtraction coefficient is calculated according to the estimated pure speech cepstrum characteristics; finally, spectral subtraction is conducted on the frequency spectrum of the noisy speech, the time-domain signal is recovered, and enhanced speech is obtained according to an overlap-add method. According to the speech enhancement method, the capability of the speech enhancement algorithm to track non-stationary noise can be improved remarkably.

Description

technical field [0001] The invention belongs to the technical field of speech recognition, and specifically relates to a speech enhancement method for estimating background noise and spectral subtraction coefficients by using a Gaussian Mixture Model (GMM: Gaussian Mixture Model), performing spectral subtraction on noisy speech, and recovering pure speech. Background technique [0002] In practical applications, voice communication will inevitably be disturbed by environmental noise. In order to reduce the impact of noise on speech signals, in speech communication, some methods need to be used to suppress noise interference, enhance useful speech signals, and increase the intelligibility of speech. These methods are speech enhancement. According to the number of voice channels, voice enhancement can be divided into single-channel voice enhancement, dual-channel voice enhancement and multi-channel voice enhancement. Although dual-channel and multi-channel speech enhancement ...

Claims

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Application Information

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IPC IPC(8): G10L15/20G10L21/0216
Inventor 吕勇
Owner HOHAI UNIV
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