INDOOR MULTI-SOUND SOURCE LOCALIZATION BASED ON NONPARAMETRIC BAYESIAN CLUSTERING

This paper deals with sound source localization and number estimation in indoor environments using a circular microphone array. Multiple sound source localization is achieved by performing single source localization at each selected time-frequency (TF) point of received signals after short-time Fourier transform. A TF point selection method is proposed to reduce the computational time, which depends on a trained SVM with power and power ratio of TF points as its features. Nonparametric Bayesian clustering is applied on the obtained DOA estimates to identify the number of active sources. The algorithm is shown to outperform others through simulations.