# Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain

arXiv: https://arxiv.org/abs/2102.11588 · HTML: https://mlsanity.com/paper/2102.11588/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network](https://mlsanity.com/paper/1710.10059/) (2017)
- [Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion](https://mlsanity.com/paper/1603.09725/) (2016)
- [Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers](https://mlsanity.com/paper/1809.10961/) (2018)
- [Audiovisual Speaker Tracking using Nonlinear Dynamical Systems with Dynamic Stream Weights](https://mlsanity.com/paper/1903.06031/) (2019)

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

- [Egocentric Auditory Attention Localization in Conversations](https://mlsanity.com/paper/2303.16024/) (2023)

## Abstract (arXiv)

Estimating the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization. Both applications benefit from a known speaker position when, for instance, applying beamforming or assigning unique speaker identities. Recently, several approaches utilizing acoustic signals augmented with visual data have been proposed for this task. However, both the acoustic and the visual modality may be corrupted in specific spatial regions, for instance due to poor lighting conditions or to the presence of background noise. This paper proposes a novel audiovisual data fusion framework for speaker localization by assigning individual dynamic stream weights to specific regions in the localization space. This fusion is achieved via a neural network, which combines the predictions of individual audio and video trackers based on their time- and location-dependent reliability. A performance evaluation using audiovisual recordings yields promising results, with the proposed fusion approach outperforming all baseline models.