Wiener filter for Noise Reduction and speech enhancement

Wiener Noise Suppressor based on Decision-Directed method with TSNR and HRNR algorithms.
Updated 23 Oct 2020

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The two-step noise reduction (TSNR) technique removes the annoying reverberation effect while maintaining the benefits of the decision-directed approach. However, classic short-time noise reduction techniques, including TSNR, introduce harmonic distortion in the enhanced speech. To overcome this problem, a method called harmonic regeneration noise reduction (HRNR) is implemented in order to refine the a priori SNR used to compute a spectral gain able to preserve the speech harmonics as proposed by Plapous et al.
("Improved Signal-to-Noise Ratio Estimation for Speech Enhancement", IEEE Transactions on ASLP, Vol. 14, Issue 6, pp. 2098 - 2108, Nov. 2006).

%% example with the noisy speech file car.wav
>> [x,fs] = audioread('car.wav');
>> [out,~] = WienerNoiseReduction(x,fs,10000) % first 10000 samples are noise samples (without speech)
>> soundsc(out(1:122000),fs)

Cite As

Pascal Scalart (2024). Wiener filter for Noise Reduction and speech enhancement (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2008a
Compatible with any release
Platform Compatibility
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Version Published Release Notes

An image as been added

1) the error that was mentionned by several users has been fixed (data was changed to ns)
2) a minimum value is introduced for the filter gain (in lines 107 and 135)
3) an amplitude normalization factor is introduced (in lines 147 and 148)

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