Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis

Victor Letzelter, Mathieu Fontaine, Mickael Chen, Patrick Perez, Slim Essid, Gaël Richard

Keywords: Multiple Choice Learning, Audio processing.

TL;DR: Resilient Multiple Choice Learning is an extension of Multiple Choice Learning approaches for conditional distribution estimation in regression settings, relying on an interpretable scoring scheme and applicable for sound source localization.

Abstract:

We introduce Resilient Multiple Choice Learning (rMCL), an extension of the MCL approach for conditional distribution estimation in regression settings where multiple targets may be sampled for each training input.
Multiple Choice Learning is a simple framework to tackle multimodal density estimation, using the Winner-Takes-All (WTA) loss for a set of hypotheses. In regression settings, the existing MCL variants focus on merging the hypotheses, thereby eventually sacrificing the diversity of the predictions. In contrast, our method relies on a novel learned scoring scheme underpinned by a mathematical framework based on Voronoi tessellations of the output space, from which we can derive a probabilistic interpretation.
After empirically validating rMCL with experiments on synthetic data, we further assess its merits on the sound source localization problem, demonstrating its practical usefulness and the relevance of its interpretation.

Submission Number: 14488