spectral subtraction
2016-08-23
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number of wind noise reduction techniques have been reviewed,
implemented and evaluated. The focus is on reducing wind noise from speech
in single channel signals. More specically a generalized version of a Spectral
Subtraction method is implemented along with a Non-Stationary version that
can estimate the noise even while speech is present. Also a Non-Negative Matrix
Factorization method is implemented. The PESQ measure, dierent variations
of the SNR and Noise Residual measure, and a subjective MUSHRA test is
used to evaluate the performance of the methods. The overall conclusion is
that the Non-Negative Matrix Factorization algorithm provides the best noise
reduction of the investigated methods. This is based on both the perceptual
and energy-based evaluation. An advantage of this method is that it does not
need a Voice Activity Detector (VAD) and only assumes a-priori information
about the wi
implemented and evaluated. The focus is on reducing wind noise from speech
in single channel signals. More specically a generalized version of a Spectral
Subtraction method is implemented along with a Non-Stationary version that
can estimate the noise even while speech is present. Also a Non-Negative Matrix
Factorization method is implemented. The PESQ measure, dierent variations
of the SNR and Noise Residual measure, and a subjective MUSHRA test is
used to evaluate the performance of the methods. The overall conclusion is
that the Non-Negative Matrix Factorization algorithm provides the best noise
reduction of the investigated methods. This is based on both the perceptual
and energy-based evaluation. An advantage of this method is that it does not
need a Voice Activity Detector (VAD) and only assumes a-priori information
about the wi
matlab
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