Cepstral Smoothing of Spectral Filter Gains for Speech Enhancement Without Musical Noise

Many speech enhancement algorithms that modify short-term spectral magnitudes of the noisy signal by means of adaptive spectral gain functions are plagued by annoying spectral outliers. In this letter, we propose cepstral smoothing as a solution to this problem. We show that cepstral smoothing can effectively prevent spectral peaks of short duration that may be perceived as musical noise. At the same time, cepstral smoothing preserves speech onsets, plosives, and quasi-stationary narrowband structures like voiced speech. The proposed recursive temporal smoothing is applied to higher cepstral coefficients only, excluding those representing the pitch information. As the higher cepstral coefficients describe the finer spectral structure of the Fourier spectrum, smoothing them along time prevents single coefficients of the filter function from changing excessively and independently of their neighboring bins, thus suppressing musical noise. The proposed cepstral smoothing technique is very effective in nonstationary noise.