利用相对传递矩阵实现混响环境中多说话人分离。
Multiple Speaker Separation from Noisy Sources in Reverberant Rooms using Relative Transfer Matrix
- 基于相对传递矩阵建模声学环境，无需定位麦克风和声源。
- 在混响+3个噪声源的4人对话场景中提升语音可懂度。
- 适用于真实房间场景，训练数据需求少，实用性强。
在强混响和多个背景噪声源环境下，同时激活的多说话人分离极具挑战。本文提出一种基于相对传递矩阵（ReTM）的新方法，该矩阵是房间相对传递函数的推广，可用于处理含噪多通道麦克风录音中的并发说话人分离。该方法具备四大优势：(i) 允许多个语音与噪声源；(ii) 考虑混响效应；(iii) 不依赖语音或噪声源位置，也不需要麦克风位置及其相对几何关系；(iv) 训练仅需较短音频片段。通过模拟实验验证了该方法在4名说话人、3个噪声源的混响房间中的语音分离能力，显著提升可懂度；同时在真实房间中也展示了其实际应用可行性。
原文摘要 · Abstract (English)
Separation of simultaneously active multiple speakers is a difficult task in environments with strong reverberation and many background noise sources. This paper uses the relative transfer matrix (ReTM), a generalization of the relative transfer function of a room, to propose a simple yet novel approach for separating concurrent speakers using noisy multichannel microphone recordings. The proposed method (i) allows multiple speech and background noise sources, (ii) includes reverberation, (iii) does not need the knowledge of the locations of speech and noise sources nor microphone locations and their relative geometry, and (iv) uses relatively small segment of recordings for training. We illustrate the speech source separation capability with improved intelligibility using a simulation study consisting of four speakers in the presence of three noise sources in a reverberant room. We also show the applicability of the method in a practical experiment in a real room.
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