Low signal to noise ratio voice endpoint detection method based on time-frequency instaneous energy spectrum

Abstract

The invention relates to a low signal to noise ratio voice endpoint detection method based on a time-frequency instantaneous energy spectrum. A traditional method used for reducing noise is not ideal in noise-reducing effect. The detection method comprises that noise-contained voice is resolved firstly and reconstructs a low frequency component which is resolved, by regarding the time-frequency instantaneous energy spectrum as a basis of the endpoint detection. After being reconstructed, the signal is dealt by an empirical mode decomposition and the screening and rejecting of an intrinsic mode function are carried out in order to gain an effective international monetary fund (IMF) component by applying a correlation coefficient threshold value principle. The IMF component gained through reconstruction undergoes a Hilbert transformation and a corresponding instantaneous energy spectrum is calculated to form a feature vector of voice endpoint detection. The result is dealt with the subframe. A former five frames instantaneous energy spectrum mean value is regarded as the instantaneous energy spectrum of the noise and a start-stop dual threshold method is adopted to detect the endpoint of the noise. The detection method has the advantages of improving the noise-contained time-frequency resolving power and improving the efficiency of the voice endpoint detection.

Description

technical field [0001] The invention belongs to the field of voice processing, and relates to a low signal-to-noise ratio voice endpoint detection method based on time-frequency instantaneous energy spectrum. Background technique [0002] Various noises will inevitably be introduced in the process of voice acquisition, transmission and communication, and the existence of noise will directly affect the clarity and intelligibility of voice. Endpoint detection of noisy speech signals to obtain the start and end points of effective speech segments plays a very important role in subsequent speech enhancement, coding and recognition. At present, the traditional endpoint detection methods mainly include average energy, average zero-crossing rate, cepstral coefficient, short-time frequency band variance, short-time energy-frequency value, cepstrum distance, autocorrelation similarity distance, information entropy and spectral entropy. But they are all based on the assumption that t...

AI Technical Summary

Problems solved by technology

But they are all based on the assumption that the speech signal has short-term linear stability, but in fact the speech signal is a nonlinear non-stationary process

At the same time, the anti-noise ability of existing methods is generally not strong, and the accuracy of speech endpoint detection is generally not ideal when the signal-to-noise ratio is low.

Dr. NE.Huang proposed a new adaptive time-frequency analysis method—Hilbert-Huang transform (HHT) in 1998, which is especially suitable for time-frequency analysis of nonlinear and non-stationary noisy speech, but there are modes State aliasing and other shortcomings, so the present invention proposes a low signal-to-noise ratio speech endpoint detection method based on time-frequency instantaneous energy spectrum

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