An improved spectral subtraction speech enhancement algorithm under non-stationary noise

The main limitation of conventional spectral subtraction algorithm is that it is based on stationary noise assumption. However, the majority of the common noise types encountered in real world are non-stationary. Moreover, the method requires a voice activity detector that might not work well under very low signal-to-noise ratio conditions. In this paper, we proposed an improved spectral subtraction algorithm for speech enhancement in non-stationary noise conditions. The proposed algorithm contains two steps. Firstly, the priori information about the spectrum of speech and noise is modeled using autoregressive model and the speech and noise codebooks are constructed. Secondly, the speech and noise are estimated in each time frame by solving a log-spectral distortion minimization problem. Consequently, the proposed algorithm can adapt to varying levels of noise even while speech is present. On the other hand, autoregressive modeling results in smooth frequency spectrums and thus reduces musical noise. Experimental results show that the proposed algorithm outperforms the conventional spectral subtraction algorithm and multiband spectral subtraction algorithm.