# DeepMUSIC: Multiple Signal Classification via Deep Learning

arXiv: https://arxiv.org/abs/1912.04357 · HTML: https://mlsanity.com/paper/1912.04357/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 13 of 13)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm](https://mlsanity.com/paper/2109.10581/) (2021)
- [SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation](https://mlsanity.com/paper/2306.02271/) (2023)
- [Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming](https://mlsanity.com/paper/2103.00328/) (2021)
- [Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB](https://mlsanity.com/paper/2501.00009/) (2024)
- [Near Field Localization via AI-Aided Subspace Methods](https://mlsanity.com/paper/2504.00599/) (2025)
- [HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing](https://mlsanity.com/paper/2510.10718/) (2025)
- [Sparse Array Design for Direction Finding using Deep Learning](https://mlsanity.com/paper/2308.04615/) (2023)
- [Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB](https://mlsanity.com/paper/2412.10644/) (2024)
- [NeuralMUSIC: A Hybrid Neural-Subspace Framework for Robot Sound Source Localization](https://mlsanity.com/paper/2606.18664/) (2026)
- [Neural Network-Assisted CLEAN for Channel Modeling in Low-SNR Regimes](https://mlsanity.com/paper/2607.27450/) (2026)
- [Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation](https://mlsanity.com/paper/2610.19578/) (2026)
- [Comparative Evaluation of Deep-Learning based DoA Estimators for Real-Time ISAC Beamforming](https://mlsanity.com/paper/2610.20326/) (2026)

## Abstract (arXiv)

This letter introduces a deep learning (DL) framework for direction-of-arrival (DOA) estimation. Previous works in DL context mostly consider a single or two target scenario which is a strong limitation in practice. Hence, in this work, we propose a DL framework for multiple signal classification (DeepMUSIC). We design multiple deep convolutional neural networks (CNNs), each of which is dedicated to a subregion of the angular spectrum. In particular, each CNN is fed with the array covariance matrix and it learns the MUSIC spectra of the corresponding angular subregion. We have shown, through simulations, that the proposed DeepMUSIC framework has superior estimation accuracy and exhibits less computational complexity in comparison with both DL and non-DL based techniques.