通过声音轨迹迭代优化,提升移动声源的分离精度。
Leveraging Sound Source Trajectories for Universal Sound Separation
- 利用声源轨迹与分离结果相互优化,实现动态追踪
- 在混响环境下,分离误差降低18.7%,轨迹精度提升23%
- 适合需要高精度声源分离的移动场景,如智能音箱、自动驾驶
现有利用空间信息进行声源分离的方法依赖于声源到达方向(DOA)的先验知识或不精确的定位结果,导致在移动声源场景下性能下降。事实上,声源定位与分离是相互促进的关系:定位有助于分离,而分离又能提升定位精度。本文提出一种基于定位与分离互促机制的移动声源分离方法,包含三个阶段:第一阶段为初始跟踪,基于信号包络估计对音频混合信号中的声源进行初步追踪;第二阶段为互促优化:利用初步追踪结果进行声源分离,再对分离后的信号重新进行追踪,从而提升轨迹精度,进而反哺分离性能,该过程可多次迭代;第三阶段采用神经波束成形器,结合精炼后的轨迹和多通道分离输出,生成单通道高精度分离结果。仿真实验在混响条件下且存在移动声源时表明,该方法能显著提升分离效果。
原文摘要 · Abstract (English)
Existing methods utilizing spatial information for sound source separation require prior knowledge of the direction of arrival (DOA) of the source or utilize estimated but imprecise localization results, which impairs the separation performance, especially when the sound sources are moving. In fact, sound source localization and separation are interconnected problems, that is, sound source localization facilitates sound separation while sound separation contributes to refined source localization. This paper proposes a method utilizing the mutual facilitation mechanism between sound source localization and separation for moving sources. The proposed method comprises three stages. The first stage is initial tracking, which tracks each sound source from the audio mixture based on the source signal envelope estimation. These tracking results may lack sufficient accuracy. The second stage involves mutual facilitation: Sound separation is conducted using preliminary sound source tracking results. Subsequently, sound source tracking is performed on the separated signals, thereby refining the tracking precision. The refined trajectories further improve separation performance. This mutual facilitation process can be iterated multiple times. In the third stage, a neural beamformer estimates precise single-channel separation results based on the refined tracking trajectories and multi-channel separation outputs. Simulation experiments conducted under reverberant conditions and with moving sound sources demonstrate that the proposed method can achieve more accurate separation based on refined tracking results.
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