Interpretable Music Categorisation Based on Fuzzy Rules and High-Level Audio Features

Music classification helps to manage song collections, recommend new music, or understand properties of genres and substyles. Until now, the corresponding approaches are mostly based on less interpretable low-level characteristics of the audio signal, or on metadata, which are not always available and require high efforts for filtering the relevant information. A listener-friendly approach may rather benefit from high-level and meaningful characteristics. Therefore, we have designed a set of high-level audio features, which is capable to replace the baseline low-level feature set without a significant decrease of classification performance. However, many common classification methods change the original feature dimensions or create complex models with lower interpretability. The advantage of the fuzzy classification is that it describes the properties of music categories in an intuitive, natural way. In this work, we explore the ability of a simple fuzzy classifier based on high-level features to predict six music genres and eight styles from our previous studies.