Sound source localization based on deep neural networks with directional activate function exploiting phase information

Abstract

This paper describes sound source localization (SSL) based on deep neural networks (DNNs) using discriminative training. A naïve DNNs for SSL can be configured as follows. Input is the frequency-domain feature used in other SSL methods, and the structure of DNNs is a fully-connected network using real numbers. The training fails because its network structure loses two important properties, i.e., the orthogonality of sub-bands and the intensity- and time-information saved in complex numbers. We solved these two problems by 1) integrating directional information at each sub-band hierarchically, and 2) designing a directional activator that could treat the complex numbers at each sub-band. Our experiments indicated that our method outperformed the naive DNN-based SSL by 20 points in terms of the block-level accuracy.