# A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods

arXiv: https://arxiv.org/abs/2507.01143 · HTML: https://mlsanity.com/paper/2507.01143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 234 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Post-Training Quantization for Vision Transformer](https://mlsanity.com/paper/2106.14156/) (2021)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events](https://mlsanity.com/paper/2306.09126/) (2023)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)
- [Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019](https://mlsanity.com/paper/2009.02792/) (2020)
- [Deep Neural Networks for Multiple Speaker Detection and Localization](https://mlsanity.com/paper/1711.11565/) (2017)
- [The Cone of Silence: Speech Separation by Localization](https://mlsanity.com/paper/2010.06007/) (2020)
- [A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection](https://mlsanity.com/paper/2101.02919/) (2021)
- [A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection](https://mlsanity.com/paper/2106.06999/) (2021)
- [A multi-room reverberant dataset for sound event localization and detection](https://mlsanity.com/paper/1905.08546/) (2019)
- [The LOCATA Challenge: Acoustic Source Localization and Tracking](https://mlsanity.com/paper/1909.01008/) (2019)
- [STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events](https://mlsanity.com/paper/2206.01948/) (2022)
- [Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks](https://mlsanity.com/paper/2006.09006/) (2020)
- [Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition](https://mlsanity.com/paper/2102.07955/) (2021)
- [Sound Event Detection and Localization with Distance Estimation](https://mlsanity.com/paper/2403.11827/) (2024)
- [First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation](https://mlsanity.com/paper/1910.04388/) (2019)
- [Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates](https://mlsanity.com/paper/1807.11094/) (2018)
- [Real-time binaural speech separation with preserved spatial cues](https://mlsanity.com/paper/2002.06637/) (2020)
- [Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection](https://mlsanity.com/paper/1604.07160/) (2016)
- [Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals](https://mlsanity.com/paper/1807.11722/) (2018)
- [The Robotic Vision Scene Understanding Challenge](https://mlsanity.com/paper/2009.05246/) (2020)
- [Semi-supervised source localization with deep generative modeling](https://mlsanity.com/paper/2005.13163/) (2020)
- [Lightweight and Optimized Sound Source Localization and Tracking Methods for Open and Closed Microphone Array Configurations](https://mlsanity.com/paper/1812.00115/) (2018)

## Built on by (most cited first, 1 of 1)

- [S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information](https://mlsanity.com/paper/2607.26047/) (2026)

## Abstract (arXiv)

Sound source localization (SSL) adds a spatial dimension to auditory perception, allowing a system to pinpoint the origin of speech, machinery noise, warning tones, or other acoustic events, capabilities that facilitate robot navigation, human-machine dialogue, and condition monitoring. While existing surveys provide valuable historical context, they typically address general audio applications and do not fully account for robotic constraints or the latest advancements in deep learning. This review addresses these gaps by offering a robotics-focused synthesis, emphasizing recent progress in deep learning methodologies. We start by reviewing classical methods such as Time Difference of Arrival (TDOA), beamforming, Steered-Response Power (SRP), and subspace analysis. Subsequently, we delve into modern machine learning (ML) and deep learning (DL) approaches, discussing traditional ML and neural networks (NNs), convolutional neural networks (CNNs), convolutional recurrent neural networks (CRNNs), and emerging attention-based architectures. The data and training strategy that are the two cornerstones of DL-based SSL are explored. Studies are further categorized by robot types and application domains to facilitate researchers in identifying relevant work for their specific contexts. Finally, we highlight the current challenges in SSL works in general, regarding environmental robustness, sound source multiplicity, and specific implementation constraints in robotics, as well as data and learning strategies in DL-based SSL. Also, we sketch promising directions to offer an actionable roadmap toward robust, adaptable, efficient, and explainable DL-based SSL for next-generation robots.