scholarly journals Single channel speech enhancement using MMSE estimation of short-time modulation magnitude spectrum

2011 ◽  
Author(s):  
Kuldip Paliwal ◽  
Belinda Schwerin ◽  
Kamil Wójcicki
Keyword(s):  
Speech Enhancement ◽  
Single Channel ◽  
Magnitude Spectrum ◽  
Time Modulation ◽  
Mmse Estimation ◽  
Short Time

Phase-Sensitive Decision-Directed SNR Estimator for Single-Channel Speech Enhancement

2017 ◽  
Vol 31 (08) ◽  
pp. 1758003
Author(s):  
Shifeng Ou ◽  
Peng Song ◽  
Ying Gao
Keyword(s):  
Speech Enhancement ◽  
Speech Processing ◽  
Single Channel ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Processing System ◽  
Phase Information ◽  
Amplitude Spectra ◽  
Phase Sensitive ◽  
Short Time

The a priori signal-to-noise ratio (SNR) plays an essential role in many speech enhancement systems. Most of the existing approaches to estimate the a priori SNR only exploit the amplitude spectra while making the phase neglected. Considering the fact that incorporating phase information into a speech processing system can significantly improve the speech quality, this paper proposes a phase-sensitive decision-directed (DD) approach for the a priori SNR estimate. By representing the short-time discrete Fourier transform (STFT) signal spectra geometrically in a complex plane, the proposed approach estimates the a priori SNR using both the magnitude and phase information while making no assumptions about the phase difference between clean speech and noise spectra. Objective evaluations in terms of the spectrograms, segmental SNR, log-spectral distance (LSD) and short-time objective intelligibility (STOI) measures are presented to demonstrate the superiority of the proposed approach compared to several competitive methods at different noise conditions and input SNR levels.

scholarly journals Single Channel Speech Enhancement Using Adaptive Soft-Thresholding with Bivariate EMD

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Md. Ekramul Hamid ◽  
Md. Khademul Islam Molla ◽  
Xin Dang ◽  
Takayoshi Nakai
Keyword(s):  
Speech Enhancement ◽  
Speech Signal ◽  
Single Channel ◽  
Complex Signal ◽  
Intrinsic Mode Functions ◽  
Noisy Speech ◽  
Mode Decomposition ◽  
Data Adaptive ◽  
Improved Performance ◽  
Short Time

This paper presents a novel data adaptive thresholding approach to single channel speech enhancement. The noisy speech signal and fractional Gaussian noise (fGn) are combined to produce the complex signal. The fGn is generated using the noise variance roughly estimated from the noisy speech signal. Bivariate empirical mode decomposition (bEMD) is employed to decompose the complex signal into a finite number of complex-valued intrinsic mode functions (IMFs). The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. Each IMF is divided into short time frames for local processing. The variance of IMF of fGn calculated within a frame is used as the reference term to classify corresponding noisy speech frame into noise and signal dominant frames. Only the noise dominant frames are soft-thresholded to reduce the noise effects. Then, all the frames as well as IMFs of speech are combined, yielding the enhanced speech signal. The experimental results show the improved performance of the proposed algorithm compared to the recently reported methods.

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