Knowledge-based Multimodal Music Similarity
Andrea Poltronieri
Music similarity is an essential aspect of music retrieval, recommendation
systems, and music analysis. Moreover, similarity is of vital interest for
music experts, as it allows studying analogies and influences among composers
and historical periods. Current approaches to musical similarity rely mainly on
symbolic content, which can be expensive to produce and is not always readily
available. Conversely, approaches using audio signals typically fail to provide
any insight about the reasons behind the observed similarity. This research
addresses the limitations of current approaches by focusing on the study of
musical similarity using both symbolic and audio content. The aim of this
research is to develop a fully explainable and interpretable system that can
provide end-users with more control and understanding of music similarity and
classification systems.