Noise estimation using mean square cross prediction error for speech enhancement

Authors:
Gang Wang;Chunguang Li;Le Dong
Affiliations:
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China;Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China;Institute of Intelligent Systems and Information Technology, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Venue:
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Year:
2010

Citing 8
Cited 0

Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation

Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Extraction of Specific Signals with Temporal Structure

Neural Computation
Adaptive two-band spectral subtraction with multi-window spectral estimation

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Subjective comparison and evaluation of speech enhancement algorithms

Speech Communication
Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model

EURASIP Journal on Applied Signal Processing
Speech Signal Enhancement Based on MAP Algorithm in the ICA Space

IEEE Transactions on Signal Processing
Evaluation of Objective Quality Measures for Speech Enhancement

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper shows the feasibility of noise extraction from noisy speech and presents a two-stage approach for speech enhancement. The preproposed mean square cross prediction error (MSCPE) based blind source extraction algorithm is utilized to extract the additive noise from the noisy speech signal in the first stage. After that a modified spectral subtraction and a modified Wiener filter approach are proposed to extract the speech signal for speech enhancement in the second stage, where all the frequency spectra of the extracted noise are utilized. Theoretical justification shows that the MSCPE-based algorithm can extract desired signal from mixed sources. Experimental results show that the averaged correlation coefficient between the extracted noise and the original additive noise are beyond 85% for Gaussian noise and beyond 75% for real-world noise at SNR = 0 dB, and the proposed speech enhancement approaches perform better than conventional methods, such as spectral subtraction and Wiener filter.