Improving trajectory localization accuracy via direction-of-arrival derivative estimation

TL;DR

This paper enhances sound source localization accuracy by integrating direction-of-arrival derivatives with DOA estimates using a neural network, especially effective at low SNR levels.

Contribution

It introduces a novel method combining DOAs with their derivatives for improved localization of moving sound sources.

Findings

Combining derivatives with DOAs improves localization accuracy.

The method performs well at low SNR levels.

Experimental validation confirms the effectiveness of the approach.

Abstract

Sound source localization is crucial in acoustic sensing and monitoring-related applications. In this paper, we do a comprehensive analysis of improvement in sound source localization by combining the direction of arrivals (DOAs) with their derivatives which quantify the changes in the positions of sources over time. This study uses the SALSA-Lite feature with a convolutional recurrent neural network (CRNN) model for predicting DOAs and their first-order derivatives. An update rule is introduced to combine the predicted DOAs with the estimated derivatives to obtain the final DOAs. The experimental validation is done using TAU-NIGENS Spatial Sound Events (TNSSE) 2021 dataset. We compare the performance of the networks predicting DOAs with derivative vs. the one predicting only the DOAs at low SNR levels. The results show that combining the derivatives with the DOAs improves the localization accuracy of moving sources.