# Position tracking of a varying number of sound sources with sliding permutation invariant training

arXiv: https://arxiv.org/abs/2210.14536 · HTML: https://mlsanity.com/paper/2210.14536/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Gauge Equivariant Convolutional Networks and the Icosahedral CNN](https://mlsanity.com/paper/1902.04615/) (2019)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks](https://mlsanity.com/paper/1703.06284/) (2017)
- [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)
- [ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection](https://mlsanity.com/paper/2010.15306/) (2020)
- [An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.13092/) (2020)
- [Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training](https://mlsanity.com/paper/2110.07124/) (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)
- [Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals](https://mlsanity.com/paper/1807.11722/) (2018)
- [Differentiable Tracking-Based Training of Deep Learning Sound Source Localizers](https://mlsanity.com/paper/2111.00030/) (2021)
- [Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization](https://mlsanity.com/paper/2103.00417/) (2021)
- [Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs](https://mlsanity.com/paper/2203.16940/) (2022)

## Built on by (most cited first, 3 of 3)

- [Speaker Embeddings to Improve Tracking of Intermittent and Moving Speakers](https://mlsanity.com/paper/2506.19875/) (2025)
- [Permutation Invariant Recurrent Neural Networks for Sound Source Tracking Applications](https://mlsanity.com/paper/2306.08510/) (2023)
- [Towards Low-Latency Tracking of Multiple Speakers With Short-Context Speaker Embeddings](https://mlsanity.com/paper/2508.14115/) (2025)

## Abstract (arXiv)

Recent data- and learning-based sound source localization (SSL) methods have shown strong performance in challenging acoustic scenarios. However, little work has been done on adapting such methods to track consistently multiple sources appearing and disappearing, as would occur in reality. In this paper, we present a new training strategy for deep learning SSL models with a straightforward implementation based on the mean squared error of the optimal association between estimated and reference positions in the preceding time frames. It optimizes the desired properties of a tracking system: handling a time-varying number of sources and ordering localization estimates according to their trajectories, minimizing identity switches (IDSs). Evaluation on simulated data of multiple reverberant moving sources and on two model architectures proves its effectiveness on reducing identity switches without compromising frame-wise localization accuracy.