# Robust DOA estimation using deep acoustic imaging

arXiv: https://arxiv.org/abs/2401.08717 · HTML: https://mlsanity.com/paper/2401.08717/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [Enhanced Sound Event Localization and Detection in Real 360-degree audio-visual soundscapes](https://mlsanity.com/paper/2401.17129/) (2024)
- [Latent Acoustic Mapping for Direction of Arrival Estimation: A Self-Supervised Approach](https://mlsanity.com/paper/2507.07066/) (2025)
- [Spectrotemporal Modulation: Efficient and Interpretable Feature Representation for Classifying Speech, Music, and Environmental Sounds](https://mlsanity.com/paper/2505.23509/) (2025)
- [CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging](https://mlsanity.com/paper/2607.01295/) (2026)

## Abstract (arXiv)

Direction of arrival estimation (DoAE) aims at tracking a sound in azimuth and elevation. Recent advancements include data-driven models with inputs derived from ambisonics intensity vectors or correlations between channels in a microphone array. A spherical intensity map (SIM), or acoustic image, is an alternative input representation that remains underexplored. SIMs benefit from high-resolution microphone arrays, yet most DoAE datasets use low-resolution ones. Therefore, we first propose a super-resolution method to upsample low-resolution microphones. Next, we benchmark DoAE models that use SIMs as input. We arrive to a model that uses SIMs for DoAE estimation and outperforms a baseline and a state-of-the-art model. Our study highlights the relevance of acoustic imaging for DoAE tasks.