Wednesday 20th of September 2017
 

Performance Evaluation of Noise Estimation Techniques for Blind Source Separation in Non Stationary Noise Environment


Mohini S. Avatade

The Noise estimation Technique plays very important role in any speech denoising algorithm. Accuracy and intelligibility of denoised speech signal is mainly affected by noise estimation process. This paper basically deals with performance evaluation of various frequency domain noise estimation methods in the scenario where, microphone receives mixture of desired signal and non stationary noise signal. The primary solution for denoising of mixture is to adapt any one of the blind source separation process such as independent component analysis which extract statistically independent source components. Due to some artifacts in ICA techniques, small amount of residual noise will remain in extracted sources. The effectiveness and performance evaluation of noise estimation techniques is carried out over residual noise along with wavelet Thresholding. Comparison of Martins , Minima controlled recursive averaging (MCRA), Improved MCRA (IMCRA), MCRA2, Spectral Minima Tracking method is done based on various, speech enhancement objective parameters such as Log-Likelihood Ratio (LLR), Segmental Signal to Noise Ratio (SNRseg), Weighted Spectral Slope (WSS), Perceptual Evaluation of speech Quality (PESQ), Itakura-Saito (IS) Ratio

Keywords: Blind Source separation, Non stationary Noise, Noise estimation Technique, Objective Quality Measures.

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ABOUT THE AUTHOR

Mohini S. Avatade
received Diploma in Electronics and Telecommunication Engg in 2004 from Government Polytechnic Pune, Completed Bachelors Degree in Electronics and Telecommunication in 2010, currently doing masters in Engg. (M.E.) in signal processing from Pune University ,will receive masters Degree in 2013.Associated with Pad. Dr. D. Y. Patil Institute of Technology since 2012. Associate Member of IETE. Area of interest are Speech Signal processing, Digital Signal Processing. Currently working on Different Speech Denoising and enhancement Algorithms.


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