色噪声下基于白化频谱重排鲁棒主成分分析的语音增强算法

基于鲁棒主成分分析(RPCA)的单通道语音增强算法是高斯白噪声环境下语音增强的一种重要处理手段，但其对低秩语音分量处理效果欠佳且无法较好地抑制色噪声。针对此问题，该文提出一种基于白化频谱重排RPCA的改进语音增强算法(WSRRPCA)，通过优化噪声白化模型，将色噪声语音增强转换成白噪声语音信号处理，利用频谱重排改进RPCA语音增强处理算法，从而获得色噪声环境下语音信号处理性能的整体提升。仿真实验表明，该算法能够较好地实现色噪声环境下的语音增强，且相对于其他算法具有更佳的噪声抑制和语音质量提升能力。

Speech Enhancement Algorithm Based on Robust Principal Component Analysis with Whitened Spectrogram Rearrangement in Colored Noise

The Robust Principal Component Analysis (RPCA) based speech enhancement algorithm plays an important role for single channel speech processing in white Gaussian noise environment, but it has a poor processing effect on low-rank speech components and can not well suppress color noise. In view of this problem, an improved speech algorithm based on Whitening Spectrum Rearrangement RPCA (WSRRPCA) is proposed in this paper, which by optimizing the noise whitening model, color noise speech enhancement is converted into white noise speech signal processing, and spectrum rearrangement is used to improve RPCA speech enhancement processing algorithm to obtain an overall improvement in speech signal processing performance in a colored noise environment. Simulation experiments show that this algorithm can better achieve speech enhancement in a colored noise environment, and has better noise suppression and speech quality improvement capabilities than other algorithms.