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

arXiv: https://arxiv.org/abs/2507.07066 · HTML: https://mlsanity.com/paper/2507.07066/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [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)
- [Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019](https://mlsanity.com/paper/2009.02792/) (2020)
- [Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training](https://mlsanity.com/paper/2110.07124/) (2021)
- [Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms](https://mlsanity.com/paper/2401.12238/) (2024)
- [Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network](https://mlsanity.com/paper/1904.12769/) (2019)
- [Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis](https://mlsanity.com/paper/2111.11882/) (2021)
- [Sound Source Distance Estimation in Diverse and Dynamic Acoustic Conditions](https://mlsanity.com/paper/2309.09288/) (2023)
- [Assessment of Self-Attention on Learned Features For Sound Event Localization and Detection](https://mlsanity.com/paper/2107.09388/) (2021)
- [A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2203.10228/) (2022)
- [Robust DOA estimation using deep acoustic imaging](https://mlsanity.com/paper/2401.08717/) (2024)

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

- [CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging](https://mlsanity.com/paper/2607.01295/) (2026)

## Abstract (arXiv)

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.