# Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2006.09006 · HTML: https://mlsanity.com/paper/2006.09006/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Sound Localization by Self-Supervised Time Delay Estimation](https://mlsanity.com/paper/2204.12489/) (2022)
- [SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing](https://mlsanity.com/paper/2506.05414/) (2025)
- [Extending GCC-PHAT using Shift Equivariant Neural Networks](https://mlsanity.com/paper/2208.04654/) (2022)
- [TF-Mamba: A Time-Frequency Network for Sound Source Localization](https://mlsanity.com/paper/2409.05034/) (2024)
- [Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs](https://mlsanity.com/paper/2203.16940/) (2022)
- [Adaptive Rotary Steering with Joint Autoregression for Robust Extraction of Closely Moving Speakers in Dynamic Scenarios](https://mlsanity.com/paper/2601.12345/) (2026)
- [Sound event localization and classification using WASN in Outdoor Environment](https://mlsanity.com/paper/2403.20130/) (2024)
- [wav2pos: Sound Source Localization using Masked Autoencoders](https://mlsanity.com/paper/2408.15771/) (2024)
- [STNet: Deep Audio-Visual Fusion Network for Robust Speaker Tracking](https://mlsanity.com/paper/2410.05964/) (2024)
- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers](https://mlsanity.com/paper/2603.23723/) (2026)
- [SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio](https://mlsanity.com/paper/2411.13179/) (2024)
- [Efficient and Microphone-Fault-Tolerant 3D Sound Source Localization](https://mlsanity.com/paper/2505.20961/) (2025)
- [SceneBind: Binding What and Where Across Vision, Audio and Language](https://mlsanity.com/paper/2607.15265/) (2026)

## Abstract (arXiv)

In this paper, we present a new single sound source DOA estimation and tracking system based on the well-known SRP-PHAT algorithm and a three-dimensional Convolutional Neural Network. It uses SRP-PHAT power maps as input features of a fully convolutional causal architecture that uses 3D convolutional layers to accurately perform the tracking of a sound source even in highly reverberant scenarios where most of the state of the art techniques fail. Unlike previous methods, since we do not use bidirectional recurrent layers and all our convolutional layers are causal in the time dimension, our system is feasible for real-time applications and it provides a new DOA estimation for each new SRP-PHAT map. To train the model, we introduce a new procedure to simulate random trajectories as they are needed during the training, equivalent to an infinite-size dataset with high flexibility to modify its acoustical conditions such as the reverberation time. We use both acoustical simulations on a large range of reverberation times and the actual recordings of the LOCATA dataset to prove the robustness of our system and its good performance even using low-resolution SRP-PHAT maps.