提出一种基于协方差相减的相对传递矩阵估计算法，提升混响环境下的语音分离效果。
Relative Transfer Matrix Estimator using Covariance Subtraction
- 利用多通道信号协方差相减，实现对独立声源相对传递矩阵的盲估计
- 在低信噪比下性能优于现有基于相对传递函数和相对传递矩阵的方法
- 适用于混响环境中的语音增强与说话人分离，实测与仿真均有效
相对传递矩阵（ReTM）作为多源多接收场景下相对传递函数的推广，在噪声环境中用于语音增强与说话人分离展现出良好性能。通过利用多通道录音的协方差矩阵进行盲估计，具有重要应用价值。本文提出一种基于协方差相减的灵活且实用的ReTM估计算法，适用于选定的一组独立声源。为验证方法的通用性，我们在混响条件下进行了说话人分离实验。在模拟与真实环境中的低信噪比场景下，分离性能与现有的基于ReTM及相对传递函数的估计算法相比表现更优。
原文摘要 · Abstract (English)
The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple receivers and sources, shows promising performance when applied to speech enhancement and speaker separation in noisy environments. Blindly estimating the ReTM of sound sources by exploiting the covariance matrices of multichannel recordings is highly beneficial for practical applications. In this paper, we use covariance subtraction to present a flexible and practically viable method for estimating the ReTM for a select set of independent sound sources. To show the versatility of the method, we validated it through a speaker separation application under reverberant conditions. Separation performance is evaluated at low signal-to-noise ratio levels in comparison with existing ReTM-based and relative transfer function-based estimators, in both simulated and real-life environments.
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