  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.
