A Time-Frequency Bins Selection Pipeline for Direction-of-Arrival Estimation Using a Single Acoustic Vector Sensor

Abstract

The case that multiple speakers are simultaneously active is usually regarded as adverse condition in the context of multi-source direction-of-arrival (DOA) estimation. Moreover, in adjacent sources scenarios, classifying DOA estimates by directions may merge adjacent sources and misclassify reverberation as sources, i.e., there is a mismatch between sources and reverberation. In order to address the open challenges mentioned above, this work introduces a pipeline for selecting reliable time-frequency (TF) bins and estimating multi-source DOAs using a single acoustic vector sensor (AVS). In the proposed pipeline, we show a viewpoint that the multiple simultaneously active sources condition can be exploited to select TF bins solely dominated by sources. The consistency of the distribution of intensity vectors (IVs) in multi-source scenarios is derived mathematically to support the proposed viewpoint. We address the mismatch between sources and reverberations in adjacent sources scenarios by focusing only on the direction with the highest intensity vector density (IVD), regardless of how many sources dominate the given TF window. Simulation and experimental results show that the proposed method can accurately and robustly estimate the multi-source DOAs in various scenarios.