# Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network

## TL;DR

This paper demonstrates that a convolutional recurrent neural network can effectively localize, detect, and track multiple moving sound sources in various acoustic environments, outperforming traditional parametric methods in tracking consistency.

## Contribution

The study extends a CRNN model to jointly localize, detect, and track moving sound sources, showing improved tracking performance over existing parametric methods in dynamic scenes.

## Key findings

- CRNN tracks multiple sources more consistently than parametric methods.
- CRNN achieves higher tracking accuracy in reverberant and anechoic conditions.
- CRNN has higher localization error but better tracking stability.

## Abstract

This paper investigates the joint localization, detection, and tracking of sound events using a convolutional recurrent neural network (CRNN). We use a CRNN previously proposed for the localization and detection of stationary sources, and show that the recurrent layers enable the spatial tracking of moving sources when trained with dynamic scenes. The tracking performance of the CRNN is compared with a stand-alone tracking method that combines a multi-source (DOA) estimator and a particle filter. Their respective performance is evaluated in various acoustic conditions such as anechoic and reverberant scenarios, stationary and moving sources at several angular velocities, and with a varying number of overlapping sources. The results show that the CRNN manages to track multiple sources more consistently than the parametric method across acoustic scenarios, but at the cost of higher localization error.