Bayesian Nonparametric Clustering for Source Counting with a Small Aperture Microphone Array

Source counting (SC) in an indoor environment is an important problem in computational auditory scene analysis. However, the problem is challenging, especially when reverberation and ambient noise are present in the environment. To address this problem, we propose an augmented Bayesian non-parametric (ABNP) clustering algorithm for source counting based on sound intensity (SI) captured by a small aperture microphone array. The core idea is to incorporate an infinite Gaussian mixture model (IGMM) and a time-frequency (TF) augmented weight selection and update scheme for sound intensity estimation. The use of IGMM enables the exemption of the maximum number of sources assumed in previous methods. Experiments on both simulated and real-world data show the improved performance by the proposed method as compared with the state of the art baseline methods.