Improved Spectral Subtraction Speech Enhancement Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 536-541 ◽  
Author(s):  
Yu Hong Liu ◽  
Dong Mei Zhou ◽  
Zhan Jun Jiang
Keyword(s):  
Auditory System ◽  
Speech Enhancement ◽  
Critical Frequency ◽  
Spectral Subtraction ◽  
Human Auditory System ◽  
Speech Distortion ◽  
Simulation Results ◽  
Enhancement Algorithm ◽  
Output Snr

The paper addresses the problems of speech distortion and residual musical noise introduced by conventional spectral subtraction (SS) method for speech enhancement. In this paper, we propose a modified SS algorithm for speech enhancement based on the masking properties of human auditory system. The algorithm computes the parameters α and β dynamically according to the masking thresholds of the critical frequency segments for each speech frame. Simulation results show that the proposed algorithm is superior to the conventional SS method, not only in the improvement of output SNR, but in the reduction of the speech distortion and residual musical noise.

A Higher Intelligibility Speech-Enhancement Algorithm

2013 ◽  
Vol 321-324 ◽  
pp. 1075-1079
Author(s):  
Peng Liu ◽  
Jian Fen Ma
Keyword(s):  
Speech Enhancement ◽  
Speech Intelligibility ◽  
Minimum Mean Square Error ◽  
A Priori ◽  
Objective Evaluation ◽  
Mean Square ◽  
Magnitude Spectrum ◽  
Gain Matrix ◽  
Speech Distortion ◽  
Enhancement Algorithm

A higher intelligibility speech-enhancement algorithm based on subspace is proposed. The majority existing speech-enhancement algorithms cannot effectively improve enhanced speech intelligibility. One important reason is that they only use Minimum Mean Square Error (MMSE) to constrain speech distortion but ignore that speech distortion region differences have a significant effect on intelligibility. A priori Signal Noise Ratio (SNR) and gain matrix were used to determine the distortion region. Then the gain matrix was modified to constrain the magnitude spectrum of the amplification distortion in excess of 6.02 dB which damages intelligibility much. Both objective evaluation and subjective audition show that the proposed algorithm does improve the enhanced speech intelligibility.

A Speech Enhancement Algorithm Based on the “Music Noise” Analysis

2014 ◽  
Vol 543-547 ◽  
pp. 2784-2787
Author(s):  
Ying Ma ◽  
Xiao Hua Zhang ◽  
Bing Lei Xing
Keyword(s):  
Speech Enhancement ◽  
Noise Analysis ◽  
Noise Pollution ◽  
Spectral Subtraction ◽  
Voice Communication ◽  
Spectrum Correction ◽  
Spectral Subtraction Method ◽  
The Voice ◽  
Enhancement Algorithm ◽  
Communication Equipment

Interference is inevitable process of voice communication will be from the surrounding environment and transmission medium noise, communication equipment, electronic noise, and other speakers. These interference makes the voice receiver received for noisy speech signal with noise pollution. According to the traditional spectral subtraction residual musical noise is too strong, the weighted processing is reduced and the power spectrum correction, spectral subtraction method was adopted to improve the traditional. According to the analysis of real speech data collection simulation, improved spectral subtraction can effectively reduce the musical noise, can satisfy the requirement of speech enhancement.

An Improved Speech Enhancement Algorithm Based on Wiener-Filtering

2014 ◽  
Vol 989-994 ◽  
pp. 2565-2568
Author(s):  
Yu Hong Liu ◽  
Dong Mei Zhou ◽  
Jing Di
Keyword(s):  
Real Time ◽  
Speech Enhancement ◽  
Moving Average ◽  
Wiener Filtering ◽  
Signal Distortion ◽  
Noise Elimination ◽  
Speech Distortion ◽  
Spectrum Shaping ◽  
Enhancement Algorithm ◽  
Real Time Application

This paper proposes an improved speech enhancement algorithm based on Wiener-Filtering, which addresses the problems of speech distortion and musical noise. The proposed algorithm adopts the masking properties of human auditory system on calculating the gain of spectrum point, in order that the signal in the enhanced speech whose energy is lower than the threshold will not be decreased further and the less distortion will be brought to enhanced speech by the trade-off between the noise elimination and speech signal distortion. What’s more, in order to eliminate the “musical noise”, a spectrum-shaping technology using averaging method between adjacent frames is adopted. And to guarantee the real-time application, two-stage moving-average strategy is adopted. The computer simulation results show that the proposed algorithm is superior to the traditional Wiener method in the low CPU cost, real-time statistics, the reduction of the speech distortion and residual musical noise.

Speech enhancement based on perceptually motivated guided spectrogram filtering

2021 ◽  
pp. 1-12
Author(s):  
Jie Wang ◽  
Linhuang Yan ◽  
Qiaohe Yang ◽  
Minmin Yuan
Keyword(s):  
Auditory System ◽  
Speech Enhancement ◽  
Speech Intelligibility ◽  
Single Channel ◽  
Noisy Environments ◽  
Human Auditory System ◽  
Residual Noise ◽  
Guided Filtering ◽  
Degraded Image ◽  
Linear Transform

In this paper, a single-channel speech enhancement algorithm is proposed by using guided spectrogram filtering based on masking properties of human auditory system when considering a speech spectrogram as an image. Guided filtering is capable of sharpening details and estimating unwanted textures or background noise from the noisy speech spectrogram. If we consider the noisy spectrogram as a degraded image, we can estimate the spectrogram of the clean speech signal using guided filtering after subtracting noise components. Combined with masking properties of human auditory system, the proposed algorithm adaptively adjusts and reduces the residual noise of the enhanced speech spectrogram according to the corresponding masking threshold. Because the filtering output is a local linear transform of the guidance spectrogram, the local mask window slides can be efficiently implemented via box filter with O(N) computational complexity. Experimental results show that the proposed algorithm can effectively suppress noise in different noisy environments and thus can greatly improve speech quality and speech intelligibility.

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