# How to Listen? Rethinking Visual Sound Localization

arXiv: https://arxiv.org/abs/2204.05156 · HTML: https://mlsanity.com/paper/2204.05156/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2022.

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

- [FlowGrad: Using Motion for Visual Sound Source Localization](https://mlsanity.com/paper/2211.08367/) (2022)
- [A Critical Assessment of Visual Sound Source Localization Models Including Negative Audio](https://mlsanity.com/paper/2410.01020/) (2024)
- [Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio Pairs](https://mlsanity.com/paper/2408.09269/) (2024)
- [Learning from Silence and Noise for Visual Sound Source Localization](https://mlsanity.com/paper/2508.21761/) (2025)

## Abstract (arXiv)

Localizing visual sounds consists on locating the position of objects that emit sound within an image. It is a growing research area with potential applications in monitoring natural and urban environments, such as wildlife migration and urban traffic. Previous works are usually evaluated with datasets having mostly a single dominant visible object, and proposed models usually require the introduction of localization modules during training or dedicated sampling strategies, but it remains unclear how these design choices play a role in the adaptability of these methods in more challenging scenarios. In this work, we analyze various model choices for visual sound localization and discuss how their different components affect the model's performance, namely the encoders' architecture, the loss function and the localization strategy. Furthermore, we study the interaction between these decisions, the model performance, and the data, by digging into different evaluation datasets spanning different difficulties and characteristics, and discuss the implications of such decisions in the context of real-world applications. Our code and model weights are open-sourced and made available for further applications.