AMBIGUITY MODELLING WITH LABEL DISTRIBUTION LEARNING FOR MUSIC CLASSIFICATION

An important amount of work has been devoted to the task of music classification. Despite promising results achieved by convolutional neural networks, there still exists a gap left to be filled for such models to perform well in real-world applications. In this work, we address the issue of ambiguity that can arise in many classification problems. We propose a method based on adaptive label smoothing that aims at implicitly modelling perceptual vagueness among classes to improve both training and testing performances. We assess our method using two state-of-the-art CNN architectures for audio classification on a variety of music mood and genre classification tasks. We show that the proposed strategy brings consistent improvements over the traditional approach, significantly improves generalization to external audio collections and emphasizes how crucial information carried by labels can be in an ambiguous music classification context.