# Iterative Sound Source Localization for Unknown Number of Sources

arXiv: https://arxiv.org/abs/2206.12273 · HTML: https://mlsanity.com/paper/2206.12273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network](https://mlsanity.com/paper/1710.10059/) (2017)
- [Deep Neural Networks for Multiple Speaker Detection and Localization](https://mlsanity.com/paper/1711.11565/) (2017)
- [ADL-MVDR: All deep learning MVDR beamformer for target speech separation](https://mlsanity.com/paper/2008.06994/) (2020)
- [Recursive speech separation for unknown number of speakers](https://mlsanity.com/paper/1904.03065/) (2019)
- [Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition](https://mlsanity.com/paper/2102.07955/) (2021)
- [A Real-time Speaker Diarization System Based on Spatial Spectrum](https://mlsanity.com/paper/2107.09321/) (2021)
- [L-SpEx: Localized Target Speaker Extraction](https://mlsanity.com/paper/2202.09995/) (2022)
- [End-to-end Binaural Sound Localisation from the Raw Waveform](https://mlsanity.com/paper/1904.01916/) (2019)
- [Deep Residual Network for Sound Source Localization in the Time Domain](https://mlsanity.com/paper/1808.06429/) (2018)
- [MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation](https://mlsanity.com/paper/2104.08450/) (2021)
- [Localization Based Sequential Grouping for Continuous Speech Separation](https://mlsanity.com/paper/2107.06853/) (2021)

## Built on by (most cited first, 1 of 1)

- [Mask-Weighted Spatial Likelihood Coding for Speaker-Independent Joint Localization and Mask Estimation](https://mlsanity.com/paper/2410.19595/) (2024)

## Abstract (arXiv)

Sound source localization aims to seek the direction of arrival (DOA) of all sound sources from the observed multi-channel audio. For the practical problem of unknown number of sources, existing localization algorithms attempt to predict a likelihood-based coding (i.e., spatial spectrum) and employ a pre-determined threshold to detect the source number and corresponding DOA value. However, these threshold-based algorithms are not stable since they are limited by the careful choice of threshold. To address this problem, we propose an iterative sound source localization approach called ISSL, which can iteratively extract each source's DOA without threshold until the termination criterion is met. Unlike threshold-based algorithms, ISSL designs an active source detector network based on binary classifier to accept residual spatial spectrum and decide whether to stop the iteration. By doing so, our ISSL can deal with an arbitrary number of sources, even more than the number of sources seen during the training stage. The experimental results show that our ISSL achieves significant performance improvements in both DOA estimation and source number detection compared with the existing threshold-based algorithms.