Confidence measures in automatic music classification

Abstract

Automatic music classification receives a steady attention in the research community. Music can be classified, for instance, according to music genre, style, mood, or played instruments. Automatically retrieved class labels can be used for searching and browsing within large digital music collections. However, due to the variability and complexity of music data and due to the imprecise class definitions, the classification of the real-world music remains error-prone. The reliability of automatic class decisions is essential for many applications. The goal of this work is to enhance the automatic class labels with confidence measures that provide an estimation of the probability of correct classification. We explore state-of-the-art classification techniques in application to automatic music genre classification and investigate to what extend posterior class probabilities can be used as confidence measures. The experimental results demonstrate some inadequacy of these confidence measures, which is very important for practical applications.