Algorithm for estimating pitch period of voice signal

Abstract

The invention discloses an algorithm for estimating a pitch period of a voice signal and relates to the field of voice signal processing. The algorithm comprises the following steps of: 1, denoising a voice signal with noise through an adaptive filter; 2, determining a self-correlation function of the denoised voice signal and a cyclic average magnitude difference function; and 3, obtaining a weighted square characteristic value through a formula, wherein alpha, beta and gamma are constants which are respectively more than 1, R (k) is the self-correlation function, and the D (k) is the average magnitude difference function. By the algorithm, the pitch period can be effectively detected in the environment with a low signal to noise ratio, the extraction errors are reduced, octave or semioctave errors are reduced, the estimation accuracy of a pitch is improved when the algorithm is sensitive to change of an amplitude or frequency of the voice signal, and the robustness is high.

Description

technical field [0001] The invention relates to the field of speech signal processing, in particular to a pitch period estimation algorithm of speech signals. Background technique [0002] Speech signal analysis is the premise and foundation of speech signal processing. Only by analyzing the parameters that can characterize the essential characteristics of the speech signal, can it be possible to use these parameters for efficient speech synthesis, speech recognition, speech compression coding, etc., where the pitch period is One of the most important characteristic parameters in speech signal processing. Pitch period refers to the period of vocal cord vibration when voiced sounds are produced. The estimation of pitch period is called pitch detection. Its purpose is to extract the trajectory curve of pitch period change that is consistent with or as close as possible to the frequency of vocal cord vibration. It is very important. [0003] Since the speech signal can be rega...

AI Technical Summary

Problems solved by technology

The ACF method is to calculate the autocorrelation function of the speech signal. The pitch is estimated by the ACF curve having a large peak at an integer multiple of the pitch period. However, as the signal-to-noise ratio decreases, it usually leads to multiplication or half-frequency errors.

The AMDF method is to calculate the average amplitude difference function of the speech signal. The pitch is estimated by the valley value of the AMDF curve at an integer multiple of the pitch period. When the amplitude or frequency of the speech signal is sensitive to changes in this method, the pitch detection accuracy is significantly reduced.

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