基于反幂法和卡尔曼滤波的自适应语音去混响方法

Adaptive Speech Dereverberation Based on Inverse Power Method and Kalman Filter

  • 摘要: 噪声鲁棒的自适应语音信号去混响是现代语音信号处理的重要研究内容,其困难在于语音信号的非白性、非平稳性及房间的超长冲激响应特性。针对单输入多输出(SIMO)麦克风阵列系统获取的多路混响语音信号,提出了一种新的去混响算法。首先通过相关法时延估计对SIMO混响语音信号进行时延对齐;其次在保持SIMO系统输出信号间交叉关联关系(cross relation)基础上对混响语音信号进行预白化处理;最后把交叉关联关系、用于矩阵最小特征向量计算的反幂法与卡尔曼滤波解卷积方法有机结合,实现了SIMO混响语音信号的实时自适应去混响。仿真与实验研究表明,本方法对混响语音信号去混响效果明显,同时具有较好的抗噪声性能。

     

    Abstract: Adaptive speech dereverberation is an important research topic in modern speech signal processing. The difficulties lie in the nonwhite and nonstationary properties of speech signals and the long impulse responses of rooms. A novel dereverberation algorithm is proposed for singleinput multiple-output (SIMO) reverberant speech signals which are acquired with microphone array. The reverberant speech signals are firstly aligned according to time-delays which is estimated with cross correlation and secondly pre-whitened with a common whitening filter for all channels so as to keep the cross relation of the pre-whitened reverberant speech signals unchanged, a real-time dereverberation approach is lastly developed with cross relation, inverse power method, which is used for the minimum eigenvector solution of a matrix, and Kalman filter. Simulations and experiments show that the proposed approach works well for speech dereverberation in noisy environments.

     

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