# Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network

arXiv: https://arxiv.org/abs/1710.10059 · HTML: https://mlsanity.com/paper/1710.10059/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2017.

## Built on by (most cited first, 27 of 27)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Egocentric Deep Multi-Channel Audio-Visual Active Speaker Localization](https://mlsanity.com/paper/2201.01928/) (2022)
- [Sound Localization by Self-Supervised Time Delay Estimation](https://mlsanity.com/paper/2204.12489/) (2022)
- [Sound Localization from Motion: Jointly Learning Sound Direction and Camera Rotation](https://mlsanity.com/paper/2303.11329/) (2023)
- [Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis](https://mlsanity.com/paper/2311.01052/) (2023)
- [PILOT: Introducing Transformers for Probabilistic Sound Event Localization](https://mlsanity.com/paper/2106.03903/) (2021)
- [Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization](https://mlsanity.com/paper/2103.00417/) (2021)
- [LocSelect: Target Speaker Localization with an Auditory Selective Hearing Mechanism](https://mlsanity.com/paper/2310.10497/) (2023)
- [Winner-takes-all learners are geometry-aware conditional density estimators](https://mlsanity.com/paper/2406.04706/) (2024)
- [TF-Mamba: A Time-Frequency Network for Sound Source Localization](https://mlsanity.com/paper/2409.05034/) (2024)
- [MIMO-DoAnet: Multi-channel Input and Multiple Outputs DoA Network with Unknown Number of Sound Sources](https://mlsanity.com/paper/2207.07307/) (2022)
- [Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs](https://mlsanity.com/paper/2203.16940/) (2022)
- [Quaternion Convolutional Neural Networks: Current Advances and Future Directions](https://mlsanity.com/paper/2307.08663/) (2023)
- [Dual input neural networks for positional sound source localization](https://mlsanity.com/paper/2308.04169/) (2023)
- [DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks](https://mlsanity.com/paper/2112.00695/) (2021)
- [Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain](https://mlsanity.com/paper/2102.11588/) (2021)
- [Iterative Sound Source Localization for Unknown Number of Sources](https://mlsanity.com/paper/2206.12273/) (2022)
- [Spatial and Semantic Embedding Integration for Stereo Sound Event Localization and Detection in Regular Videos](https://mlsanity.com/paper/2507.04845/) (2025)
- [Integrating Spatial and Semantic Embeddings for Stereo Sound Event Localization in Videos](https://mlsanity.com/paper/2509.06598/) (2025)
- [What Makes Sound Event Localization and Detection Difficult? Insights from Error Analysis](https://mlsanity.com/paper/2107.10469/) (2021)
- [Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection](https://mlsanity.com/paper/2110.06126/) (2021)
- [Feature Aggregation in Joint Sound Classification and Localization Neural Networks](https://mlsanity.com/paper/2310.19063/) (2023)
- [Audio Simulation for Sound Source Localization in Virtual Evironment](https://mlsanity.com/paper/2404.01611/) (2024)
- [An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation](https://mlsanity.com/paper/2501.10755/) (2025)
- [Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD](https://mlsanity.com/paper/2508.00307/) (2025)
- [PhysWave: Physics-Guided Latent Diffusion Models for Controllable Spatial Audio Generation](https://mlsanity.com/paper/2608.29549/) (2026)
- [RAO-Nav: Probing Omni-Language Models for Zero-shot Semantic Audio-Visual Navigation](https://mlsanity.com/paper/2609.32224/) (2026)

## Abstract (arXiv)

This paper proposes a deep neural network for estimating the directions of arrival (DOA) of multiple sound sources. The proposed stacked convolutional and recurrent neural network (DOAnet) generates a spatial pseudo-spectrum (SPS) along with the DOA estimates in both azimuth and elevation. We avoid any explicit feature extraction step by using the magnitudes and phases of the spectrograms of all the channels as input to the network. The proposed DOAnet is evaluated by estimating the DOAs of multiple concurrently present sources in anechoic, matched and unmatched reverberant conditions. The results show that the proposed DOAnet is capable of estimating the number of sources and their respective DOAs with good precision and generate SPS with high signal-to-noise ratio.