Overlapping sound event detection with supervised Nonnegative Matrix Factorization
Victor Bisot, Slim Essid, Gaël Richard · 2017
In this paper we propose a supervised Nonnegative Matrix Factorization (NMF) model for overlapping sound event detection in real life audio. We start by highlighting the usefulness of non-euclidean NMF to learn representations for detecting and classifying acoustic events in a multi-label setting. Then, we propose to learn a classifier and the NMF decomposition in a joint optimization problem. This is done with a general β-divergence version of the nonnegative task-driven dictionary learning model. An experimental evaluation is performed on the development set of the DCASE 2016 task3 challenge. The proposed supervised NMF-based system improves performance over the baseline and the submitted systems.