# Sound field decomposition based on two-stage neural networks

arXiv: https://arxiv.org/abs/2309.06661 · HTML: https://mlsanity.com/paper/2309.06661/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition](https://mlsanity.com/paper/2102.07955/) (2021)
- [Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates](https://mlsanity.com/paper/1807.11094/) (2018)
- [Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1904.08452/) (2019)
- [Multiple scattering ambisonics: three-dimensional sound field estimation using interacting spheres](https://mlsanity.com/paper/2106.07157/) (2021)

## Abstract (arXiv)

A method for sound field decomposition based on neural networks is proposed. The method comprises two stages: a sound field separation stage and a single-source localization stage. In the first stage, the sound pressure at microphones synthesized by multiple sources is separated into one excited by each sound source. In the second stage, the source location is obtained as a regression from the sound pressure at microphones consisting of a single sound source. The estimated location is not affected by discretization because the second stage is designed as a regression rather than a classification. Datasets are generated by simulation using Green's function, and the neural network is trained for each frequency. Numerical experiments reveal that, compared with conventional methods, the proposed method can achieve higher source-localization accuracy and higher sound-field-reconstruction accuracy.