# Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1904.08452 · HTML: https://mlsanity.com/paper/1904.08452/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs](https://mlsanity.com/paper/2203.16940/) (2022)
- [Spatial HuBERT: Self-supervised Spatial Speech Representation Learning for a Single Talker from Multi-channel Audio](https://mlsanity.com/paper/2310.10922/) (2023)
- [Learning Robust Spatial Representations from Binaural Audio through Feature Distillation](https://mlsanity.com/paper/2508.20914/) (2025)
- [Sound field decomposition based on two-stage neural networks](https://mlsanity.com/paper/2309.06661/) (2023)

## Abstract (arXiv)

We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels. We also describe an improved method to generate synthetic data to train the neural network using state-of-the-art sound propagation algorithms that model specular as well as diffuse reflections of sound. We compare our model against three other CRNNs trained using different formulations of the same problem: classification on categorical labels, and regression on spherical coordinate labels. In practice, our model achieves up to 43% decrease in angular error over prior methods. The use of diffuse reflection results in 34% and 41% reduction in angular prediction errors on LOCATA and SOFA datasets, respectively, over prior methods based on image-source methods. Our method results in an additional 3% error reduction over prior schemes that use classification based networks, and we use 36% fewer network parameters.