arXiv:2411.03168eess.AS2024-11被引 2

通过归一化ℓp范数选择参考麦克风,提升远场语音去混响效果。

Reference Microphone Selection for the Weighted Prediction Error Algorithm using the Normalized L-p Norm

  • 用归一化ℓp范数评估输出信号,自动选最优参考麦克风
  • 实验显示在混响实验室中性能优于传统功率或早期/晚期比方法
  • 适合远场语音处理、麦克风阵列部署场景

混响会严重降低室内麦克风录制的语音质量。对于紧凑型麦克风阵列,参考麦克风的选择对去混响性能影响较小;而当麦克风空间分布较远时,参考麦克风的选择显著影响性能。本文提出基于去混响输出信号归一化ℓp范数的参考麦克风选择方法,用于加权预测误差(WPE)去混响算法。在混响实验室中不同声源位置的实验结果表明,该方法相比基于早期/晚期混响比或信号功率的参考麦克风选择,能获得更优的去混响性能。

原文摘要 · Abstract (English)

Reverberation may severely degrade the quality of speech signals recorded using microphones in a room. For compact microphone arrays, the choice of the reference microphone for multi-microphone dereverberation typically does not have a large influence on the dereverberation performance. In contrast, when the microphones are spatially distributed, the choice of the reference microphone may significantly contribute to the dereverberation performance. In this paper, we propose to perform reference microphone selection for the weighted prediction error (WPE) dereverberation algorithm based on the normalized $\ell_p$-norm of the dereverberated output signal. Experimental results for different source positions in a reverberant laboratory show that the proposed method yields a better dereverberation performance than reference microphone selection based on the early-to-late reverberation ratio or signal power.

语音去混响麦克风选择WPE

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