Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Over-complete dictionary constructing method applicable to voice compression sensing

An over-complete dictionary and compressed sensing technology, applied in speech analysis, instruments, etc., can solve problems such as insufficient sparsity of speech signals, poor reconstruction signal performance, poor reconstruction performance, etc., and achieve a simple and good method of constructing transformation matrix Sparsity, the effect of increasing sparsity

Inactive Publication Date: 2013-01-16
NANJING UNIV OF POSTS & TELECOMM
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When the speech signal is reconstructed using discrete cosine transform (DCT) base and wavelet base for single-scale non-adaptive compressed sensing, when the number of observations is half or less than the original value, the reconstruction performance is very good. Poor, mainly because the sparsity of the speech signal in the conventional orthogonal basis is not good enough, resulting in poor performance of the reconstructed signal

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
  • Over-complete dictionary constructing method applicable to voice compression sensing
  • Over-complete dictionary constructing method applicable to voice compression sensing
  • Over-complete dictionary constructing method applicable to voice compression sensing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0033] The present invention provides an over-complete dictionary construction method suitable for speech compression sensing, including three steps of constructing a linear prediction (Linear Prediction, LP) matrix, constructing an over-complete LP dictionary, and speech CS compression sampling and reconstruction, which are given below The main implementation of these three steps.

[0034] A. Construct LP matrix

[0035] Using conventional orthogonal bases (such as Fourier transform bases, DCT bases or wavelet transform bases) to reconstruct speech signals with low compression ratios cannot achieve good performance, mainly because the sparsity of speech signals under conventional orthogonal bases is not good enough Therefore, the present invention uses linear prediction to improve the sparsity of the signal. Using the correlation between...

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 an over-complete dictionary constructing method applicable to voice compressed sensing. The over-complete dictionary constructing method includes firstly, constructing over-complete linear prediction dictionary by a great quantity of training voices during training, subjecting voice signals to CS (compressed sensing) sampling by utilizing a random Gaussian matrix as an observation matrix in a real testing stage; and finally reconstructing the voice signals in high quality by adopting the BP algorithm on the basis of the linear prediction dictionary. Without a prediction system for testing voice, the over-complete dictionary constructing method is simple in construction, the voice signals are good in sparsity in the over-complete linear prediction dictionary, and voice compressed sensing reconstruction signals based on the over-complete linear prediction dictionary are excellent in performance and have better robustness.

Description

technical field [0001] The invention belongs to the field of signal sampling and speech signal processing, in particular to a new method for constructing an over-complete dictionary suitable for speech compression sensing. Background technique [0002] Speech is the most convenient and direct communication method for human beings. Traditional speech signal processing is based on Nyquist (Nyquist) sampling theorem. First, high-speed sampling with more than 2 times the bandwidth is performed, and then recompressed according to the strong correlation between samples. , this process wastes a lot of sampling resources. The Nyquist sampling theorem is the law followed by most signal sampling, which shows the relationship between the sampling frequency and the signal spectrum distribution. It is a sufficient condition for accurate reconstruction of any signal, but it is not necessarily a necessary condition. How to remodel the speech signal according to the particularity of the sp...

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): G10L19/12
Inventor 孙林慧杨震杨真真
Owner NANJING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com