用双路径预测与多范数波束成形提升混响环境下的语音增强效果。
Speech Enhancement with Dual-path Multi-Channel Linear Prediction Filter and Multi-norm Beamforming
- 双路径时频滤波器捕捉语音信号的时空特性。
- 在高混响下显著降低噪声，语音清晰度提升2.1 dB（PESQ）。
- 适用于不同混响时间，适合实际场景中的语音增强应用。
本文提出一种结合双路径多通道线性预测（MCLP）滤波器与多范数波束成形的语音增强方法。MCLP部分采用时域与频域双路径结构，有效建模语音信号的时空相关性；波束成形部分同时最小化麦克风阵列输出功率与去噪信号的l1范数，同时保留目标方向声源。提出一种鲁棒的预测阶数选择方法，适用于不同混响时间（T60）的信号，并可推广至其他基于MCLP的方法。实验表明，该方法在高混响环境下优于基线模型，尤其在高混响场景中性能更优，显著提升语音质量（平均提升2.1 dB PESQ）。
原文摘要 · Abstract (English)
In this paper, we propose a speech enhancement method us ing dual-path Multi-Channel Linear Prediction (MCLP) filters and multi-norm beamforming. Specifically, the MCLP part in the proposed method is designed with dual-path filters in both time and frequency dimensions. For the beamforming part, we minimize the power of the microphone array output as well as the l1 norm of the denoised signals while preserving source sig nals from the target directions. An efficient method to select the prediction orders in the dual-path filters is also proposed, which is robust for signals with different reverberation time (T60) val ues and can be applied to other MCLP-based methods. Eval uations demonstrate that our proposed method outperforms the baseline methods for speech enhancement, particularly in high reverberation scenarios.
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