  Data-based and learning-based sound source localization (SSL) has shown
promising results in challenging conditions, and is commonly set as a
classification or a regression problem. Regression-based approaches have
certain advantages over classification-based, such as continuous
direction-of-arrival estimation of static and moving sources. However,
multi-source scenarios require multiple regressors without a clear training
strategy up-to-date, that does not rely on auxiliary information such as
simultaneous sound classification. We investigate end-to-end training of such
methods with a technique recently proposed for video object detectors, adapted
to the SSL setting. A differentiable network is constructed that can be plugged
to the output of the localizer to solve the optimal assignment between
predictions and references, optimizing directly the popular CLEAR-MOT tracking
metrics. Results indicate large improvements over directly optimizing mean
squared errors, in terms of localization error, detection metrics, and tracking
capabilities.
