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.

翻译：活跃声源数量是许多声学信号处理任务中的关键参数，例如声源定位、声源分离和多麦克风语音增强。本文提出一种基于空间相干性检测活跃声源数量变化的在线声源计数新方法。该方法利用以下事实：在空间白背景噪声中，单个相干声源会产生高空间相干性，而仅有噪声则导致低空间相干性。通过应用空间白化操作，声源计数问题被重新定义为变化检测任务，旨在识别活跃声源数量发生变化的时间帧。该方法采用广义幅度平方相干性作为量化空间相干性的度量，为经过训练的紧凑神经网络提供帧级声源数量变化检测特征。在混响声学场景中，使用双耳助听器对最多4个说话人和背景噪声进行的仿真结果表明，所提方法在在线声源计数方面具有有效性。