# DOA Estimation with Lightweight Network on LLM-Aided Simulated Acoustic Scenes

arXiv: https://arxiv.org/abs/2511.08012 · HTML: https://mlsanity.com/paper/2511.08012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 13 of 13)

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- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.13092/) (2020)
- [Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation](https://mlsanity.com/paper/2410.10676/) (2024)
- [Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.00140/) (2020)
- [A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning](https://mlsanity.com/paper/2208.08082/) (2022)
- [Robust Sound Source Localization Using a Microphone Array on a Mobile Robot](https://mlsanity.com/paper/1602.08213/) (2016)

## Abstract (arXiv)

Direction-of-Arrival (DOA) estimation is critical in spatial audio and acoustic signal processing, with wide-ranging applications in real-world. Most existing DOA models are trained on synthetic data by convolving clean speech with room impulse responses (RIRs), which limits their generalizability due to constrained acoustic diversity. In this paper, we revisit DOA estimation using a recently introduced dataset constructed with the assistance of large language models (LLMs), which provides more realistic and diverse spatial audio scenes. We benchmark several representative neural-based DOA methods on this dataset and propose LightDOA, a lightweight DOA estimation model based on depthwise separable convolutions, specifically designed for mutil-channel input in varying environments. Experimental results show that LightDOA achieves satisfactory accuracy and robustness across various acoustic scenes while maintaining low computational complexity. This study not only highlights the potential of spatial audio synthesized with the assistance of LLMs in advancing robust and efficient DOA estimation research, but also highlights LightDOA as efficient solution for resource-constrained applications.