# Exploiting Deep Neural Networks and Head Movements for Robust Binaural Localisation of Multiple Sources in Reverberant Environments

## TL;DR

This paper introduces a deep neural network-based binaural sound localisation system that uses head movements to accurately identify multiple sound sources in reverberant environments, outperforming traditional methods.

## Contribution

The study presents a novel DNN approach that leverages binaural cues and head movements for robust multi-source localisation, addressing front-back confusions in reverberant settings.

## Key findings

- DNN exploits additional information in the cross-correlation function.
- Head movements significantly reduce front-back localisation errors.
- The proposed system outperforms GMM-based methods in challenging acoustic scenarios.

## Abstract

This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for robust binaural localisation of multiple sources in reverberant environments. DNNs are used to learn the relationship between the source azimuth and binaural cues, consisting of the complete cross-correlation function (CCF) and interaural level differences (ILDs). In contrast to many previous binaural hearing systems, the proposed approach is not restricted to localisation of sound sources in the frontal hemifield. Due to the similarity of binaural cues in the frontal and rear hemifields, front-back confusions often occur. To address this, a head movement strategy is incorporated in the localisation model to help reduce the front-back errors. The proposed DNN system is compared to a Gaussian mixture model (GMM) based system that employs interaural time differences (ITDs) and ILDs as localisation features. Our experiments show that the DNN is able to exploit information in the CCF that is not available in the ITD cue, which together with head movements substantially improves localisation accuracies under challenging acoustic scenarios in which multiple talkers and room reverberation are present.