Latent Acoustic Mapping for Direction of Arrival Estimation: A Self-Supervised Approach

TL;DR

This paper introduces Latent Acoustic Mapping (LAM), a self-supervised model that combines interpretability and adaptability for improved direction of arrival estimation in spatial audio, outperforming traditional and supervised methods.

Contribution

The paper presents a novel self-supervised framework, LAM, that produces high-resolution acoustic maps, adapts to diverse acoustic conditions, and enhances DoAE performance across various setups.

Findings

LAM achieves comparable or better localization accuracy than supervised methods.

LAM's acoustic maps improve supervised DoAE models when used as features.

The approach demonstrates robustness across different acoustic environments and microphone arrays.

Abstract

Acoustic mapping techniques have long been used in spatial audio processing for direction of arrival estimation (DoAE). Traditional beamforming methods for acoustic mapping, while interpretable, often rely on iterative solvers that can be computationally intensive and sensitive to acoustic variability. On the other hand, recent supervised deep learning approaches offer feedforward speed and robustness but require large labeled datasets and lack interpretability. Despite their strengths, both methods struggle to consistently generalize across diverse acoustic setups and array configurations, limiting their broader applicability. We introduce the Latent Acoustic Mapping (LAM) model, a self-supervised framework that bridges the interpretability of traditional methods with the adaptability and efficiency of deep learning methods. LAM generates high-resolution acoustic maps, adapts to varying acoustic conditions, and operates efficiently across different microphone arrays. We assess its robustness on DoAE using the LOCATA and STARSS benchmarks. LAM achieves comparable or superior localization performance to existing supervised methods. Additionally, we show that LAM's acoustic maps can serve as effective features for supervised models, further enhancing DoAE accuracy and underscoring its potential to advance adaptive, high-performance sound localization systems.