Voice segment detection method based on MFCC similarity of EMD-Wavelet

A detection method and similarity technology, applied in speech analysis, instruments, etc., can solve the problems of MFCC effect not obvious, and achieve the effect of good robustness and adaptability, suppression of influence, and high accuracy

Active Publication Date: 2019-03-01
SOUTHEAST UNIV
View PDF9 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although MFCC can well reflect the signal characteristics of speech signals, in the case of low signal-to-noise ratio, the effect of MFCC for speech segment detection is not obvious

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Voice segment detection method based on MFCC similarity of EMD-Wavelet
  • Voice segment detection method based on MFCC similarity of EMD-Wavelet
  • Voice segment detection method based on MFCC similarity of EMD-Wavelet

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0050] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0051] see figure 1 , figure 1 is a flowchart of the present invention. The steps of the present invention will be described in detail below in conjunction with the flowchart.

[0052] A kind of speech segment detection method based on the MFCC similarity of EMD-Wavelet, the method comprises the steps:

[0053] (1) Measure the voice signal of people speaking, and use the collected voice signal as the source signal;

[0054] (2) EMD is used to decompose the noisy speech signal to obtain the intrinsic mode function (IMF) of each order reflecting the high and low frequency energy of the speech signal. The specific process is as follows:

[0055] (2.1) First find out the local extremum points of the overall signal, and use cubic spline interpolation to form the upper envelope and lower envelope of the signal respectively.

[0056] (2.2) Calcul...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a voice segment detection method based on MFCC similarity of EMD-Wavelet. The method includes: collecting a speaker's voice signal as the source signal; decomposing a noisy voice signal by empirical mode decomposition (EMD) to obtain an all-order intrinsic mode function (IMF); determining the order of a noise-dominated mode IMF by the variance of all-order IMF components' autocorrelation coefficients, conducting wavelet thresholding denoising on the noise-dominated mode IMF, performing reconstruction with the denoised low-order IMF component and remaining high-order IMFcomponent, thus obtaining a de-noised voice signal; calculating the Mel frequency cepstrum coefficient (MFCC) of the voice signal, and adopting Euclidean distance as the measure of the voice signal MFCC similarity; and obviously distinguishing a voice segment and a noise segment from a similarity curve, thus realizing voice segment extraction. Compared with traditional detection methods, the method provided by the invention has better robustness and adaptability, higher voice segment detection accuracy, and can be well applied to voice segment extraction of voice signals.

Description

Technical field: [0001] The invention relates to a method for detecting speech segments based on MFCC similarity of EMD-Wavelet, and belongs to the field of detecting speech segments in speech signal processing. Background technique: [0002] Speech segment detection is an important part of speech signal analysis and processing. Its purpose is to extract the speech segment of the speech signal from a segment of signal containing speech. The detection accuracy directly affects the processing time and calculation amount of the speech signal. Therefore, improving the accuracy and efficiency of the detection of the start and end points of speech segments has always been a hot spot in the research of speech recognition technology. Traditional speech segment detection algorithms often use methods such as short-term energy, short-term zero-crossing rate, and autocorrelation maximum. These methods have achieved better detection results in the case of high SNR. Down detection is poo...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G10L21/0208G10L25/24G10L25/51G10L25/78
CPCG10L21/0208G10L25/24G10L25/51G10L25/78
Inventor 贾民平花园胡建中许飞云黄鹏
Owner SOUTHEAST UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap