通过空间相干性变化检测，实时判断声源数量
DNN-Based Online Source Counting Based on Spatial Generalized Magnitude Squared Coherence
- 利用广义幅度平方相干性衡量空间相干性，重构为变化检测任务
- 在混响环境下支持最多4个说话人，准确识别源数变化时刻
- 适用于助听器等实时音频系统，对多源场景有良好适应性
声源数量是声信号处理中的关键参数，涉及声源定位、分离和多麦克风语音增强等任务。本文提出一种基于空间相干性的在线源计数新方法，通过检测活跃声源数量的变化来实现。该方法利用单个相干声源在空间白噪声背景下产生高空间相干性，而仅噪声时相干性低的特性，结合空间去相关操作，将源计数问题转化为变化检测任务。采用广义幅度平方相干性作为度量，提取特征并输入紧凑神经网络，实现逐帧源数变化检测。仿真结果表明，在混响环境下，该方法在最多4个说话人及背景噪声条件下均表现出色，验证了其在实时源计数中的有效性。
原文摘要 · Abstract (English)
The number of active sound sources is a key parameter in many acoustic signal processing tasks, such as source localization, source separation, and multi-microphone speech enhancement. This paper proposes a novel method for online source counting by detecting changes in the number of active sources based on spatial coherence. The proposed method exploits the fact that a single coherent source in spatially white background noise yields high spatial coherence, whereas only noise results in low spatial coherence. By applying a spatial whitening operation, the source counting problem is reformulated as a change detection task, aiming to identify the time frames when the number of active sources changes. The method leverages the generalized magnitude-squared coherence as a measure to quantify spatial coherence, providing features for a compact neural network trained to detect source count changes framewise. Simulation results with binaural hearing aids in reverberant acoustic scenes with up to 4 speakers and background noise demonstrate the effectiveness of the proposed method for online source counting.
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