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

arXiv: https://arxiv.org/abs/1904.12769 · HTML: https://mlsanity.com/paper/1904.12769/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization](https://mlsanity.com/paper/2103.00417/) (2021)
- [Multi-goal Audio-visual Navigation using Sound Direction Map](https://mlsanity.com/paper/2308.00219/) (2023)
- [Winner-takes-all learners are geometry-aware conditional density estimators](https://mlsanity.com/paper/2406.04706/) (2024)
- [Enhanced Sound Event Localization and Detection in Real 360-degree audio-visual soundscapes](https://mlsanity.com/paper/2401.17129/) (2024)
- [HiSSNet: Sound Event Detection and Speaker Identification via Hierarchical Prototypical Networks for Low-Resource Headphones](https://mlsanity.com/paper/2303.07538/) (2023)
- [Latent Acoustic Mapping for Direction of Arrival Estimation: A Self-Supervised Approach](https://mlsanity.com/paper/2507.07066/) (2025)
- [SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios](https://mlsanity.com/paper/2607.02343/) (2026)
- [Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD](https://mlsanity.com/paper/2508.00307/) (2025)
- [SonicWorld: Source-Centric Latent Dynamics for Spatial Audio Reasoning](https://mlsanity.com/paper/2610.17131/) (2026)

## Abstract (arXiv)

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.