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Design and Implementation of Noise Free Audio Speech Signal Using Fast Block Least Mean Square Algorithm

Author(s): J. Jebastine | B. Sheela Rani

Journal: Signal & Image Processing
ISSN 2229-3922

Volume: 3;
Issue: 3;
Start page: 39;
Date: 2012;
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Keywords: Noise cancellation | FBLMS Adaptive Algorithm | Simulink Model | SNR.

This paper describes the development of an adaptive noise cancellation algorithm for effective recognition of speech signal and also to improve SNR for an adaptive step size input. An adaptive filter with Fast Block Least Mean square Algorithm is designed for noise free audio (speech/music) signals. The signal input used is a audio speech signal which could be in the form of a recorded voice. The filter used is adaptive filter and the algorithm used is Fast Block LMS algorithm. A Gaussian noise is added to this input signal and given as a input to the Fast Block LMS. The algorithm is implemented in Matlab and was tested fornoise cancellation in speech signals. A Simulink model is designed which results in a noise free audiospeech signal at the output. The FBLMS algorithm is computationally efficient in noise cancellation. The noise level in speech signal can be 1) mild, 2) moderate, 3) severe. The SNR is estimated by varying the adaptive step size.
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