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

2025-07-01

AI Summary

Existing surveys on sound source localization (SSL) primarily target generic audio scenarios, overlooking robotics-specific constraints and recent advances in deep learning. This paper presents the first systematic review of deep learning–based SSL methods tailored for robotic applications. We comprehensively analyze architectures integrating time-delay estimation, beamforming, CNNs, CRNNs, and attention mechanisms, while explicitly incorporating robotic platform characteristics and typical tasks—including navigation and human–robot interaction—to formulate a robust, efficient, and interpretable SSL research framework. Key challenges are identified: environmental non-stationarity, multi-source separation, real-time processing, and resource-constrained deployment. We establish a taxonomy of prior work organized by robot type and application scenario, and delineate critical technical pathways—including environment-aware data construction, weakly supervised training, and model lightweighting. Our synthesis provides both theoretical foundations and practical guidelines for deploying SSL in real-world robotic systems.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• Reviews deep learning methods for sound source localization in robotics
• Addresses gaps in robotic constraints and latest DL advancements
• Highlights challenges like environmental robustness and data strategies

Innovation

• Deep learning methods for sound localization
• Robotics-focused synthesis of SSL techniques
• Attention-based architectures in SSL