# Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise

arXiv: https://arxiv.org/abs/1712.04276 · HTML: https://mlsanity.com/paper/1712.04276/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

## Built on by (most cited first, 4 of 4)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [A Deep Reinforcement Learning Approach to Audio-Based Navigation in a Multi-Speaker Environment](https://mlsanity.com/paper/2105.04488/) (2021)
- [A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments](https://mlsanity.com/paper/2110.12778/) (2021)
- [LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy](https://mlsanity.com/paper/2302.09409/) (2023)

## Abstract (arXiv)

The problem of multi-speaker localization is formulated as a multi-class multi-label classification problem, which is solved using a convolutional neural network (CNN) based source localization method. Utilizing the common assumption of disjoint speaker activities, we propose a novel method to train the CNN using synthesized noise signals. The proposed localization method is evaluated for two speakers and compared to a well-known steered response power method.