统一设计回声消除与降噪算法,提升语音录制质量。
Integrated Minimum Mean Squared Error Algorithms for Combined Acoustic Echo Cancellation and Noise Reduction
- 构建统一信号模型与代价函数,联合优化回声消除与降噪。
- 提出扩展多通道维纳滤波器,性能优于传统级联方法。
- 适用于多麦克风/多扬声器场景,适合实际语音处理系统。
在许多语音录音应用中,噪声和声学回声会干扰目标语音。因此需要联合进行噪声抑制(NR)与声学回声消除(AEC)。通常采用级联方式,即分别设计独立的AEC与NR模块,使用不同的信号模型、代价函数和求解策略,再依次连接,忽略了二者间的相互影响。本文提出一种集成方法,在多麦克风/多扬声器设置下考虑两者的交互作用。采用单一麦克风信号向量或麦克风与扬声器信号拼接后的扩展信号向量作为信号模型,定义统一的均方误差代价函数,并使用共同求解策略。基于麦克风信号模型,推导出多通道维纳滤波器(MWF)。基于扩展信号模型,进一步推导出扩展多通道维纳滤波器(MWFext),并发现多个等价表达式,可解释为级联算法:包括先回声消除后降噪(AEC-NR)、先降噪后回声消除(NR-AEC)、以及扩展降噪(NRext)先于回声消除与后置滤波器(PF)(NRext-AEC-PF)。在秩不足条件下,MWFext不唯一,其等价性体现为特定非最小范数解。实际性能因非平稳性和相关矩阵估计不准确而异,其中AEC-NR与NRext-AEC-PF表现最优。
原文摘要 · Abstract (English)
In many speech recording applications, noise and acoustic echo corrupt the desired speech. Consequently, combined noise reduction (NR) and acoustic echo cancellation (AEC) is required. Generally, a cascade approach is followed, i.e., the AEC and NR are designed in isolation by selecting a separate signal model, separate cost function, and separate solution strategy. The AEC and NR are then cascaded one after the other, not accounting for their interaction. In this paper, an integrated approach is proposed to consider this interaction in a general multi-microphone/multi-loudspeaker setup. Therefore, a single signal model of either the microphone signal vector or the extended signal vector, obtained by stacking microphone and loudspeaker signals, is selected, a single mean squared error cost function is formulated, and a common solution strategy is used. Using this microphone signal model, a multi-channel Wiener filter (MWF) is derived. Using the extended signal model, it is shown that an extended MWF (MWFext) can be derived, and several equivalent expressions can be found, which are nevertheless shown to be interpretable as cascade algorithms. Specifically, the MWFext is shown to be equivalent to algorithms where the AEC precedes the NR (AEC-NR), the NR precedes the AEC (NR-AEC), and the extended NR (NRext) precedes the AEC and post-filter (PF) (NRext-AEC-PF). Under rank-deficiency conditions the MWFext is non-unique. Equivalence then amounts to the expressions being specific, not necessarily minimum-norm solutions, for this MWFext. The practical performances differ due to non-stationarities and imperfect correlation matrix estimation, with the AEC-NR and NRext-AEC-PF attaining best overall performance.
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