关键词: 语音增强, 谱减法, 噪声估计, 自回归模型

Abstract: An improved spectral subtraction algorithm based on codebook learning for speech enhancement in non-stationary noise conditions is proposed. The proposed algorithm contains two steps. The priori information about the spectrum of speech and noise is modeled using autoregressive model and the speech and noise codebooks are constructed. The speech and noise are estimated in each time frame by solving a log-spectral distortion minimization problem. 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 traditional spectral subtraction algorithm and multiband spectral subtraction algorithm.

Key words: speech enhancement, spectral subtraction, noise estimation, auto regressive model