Speech Enhancement using Spectral Subtraction-type Algorithms: A Comparison and Simulation Study

The spectral subtraction is historically one of the first algorithms proposed for the enhancement of single channel speech. In this method, the noise spectrum is estimated during speech pauses, and is subtracted from the noisy speech spectrum to estimate the clean speech. This is also achieved by multiplying the noisy speech spectrum with a gain function and later combining it with the phase of the noisy speech. The drawback of this method is the presence of processing distortions, called remnant noise. A number of variations of the method have been developed over the past years to address the drawback. These variants form a family of spectral subtractive-type algorithms. The aim of this paper is to provide a comparison and simulation study of the different forms of subtraction-type algorithms viz. basic spectral subtraction, spectral over-subtraction, multi-band spectral subtraction, Wiener filtering, iterative spectral subtraction, and spectral subtraction based on perceptual properties. To test the performance of the subtractive-type algorithms, the objective measures (SNR and PESQ), spectrograms and informal listening tests are conducted for both stationary and non-stationary noises types at different SNRs levels. It is evident from the results that the modified forms of spectral subtraction method reduces remnant noise significantly and the enhanced speech contains minimal speech distortion.