Music Similarity

Music similarity research aims to develop robust methods for quantifying how alike different musical pieces or segments are, enabling applications like music recommendation and plagiarism detection. Current research focuses on developing sophisticated models, including neural networks (e.g., transformers, Joint-Embedding Predictive Architectures) and metric learning approaches, to capture diverse aspects of musical similarity, such as melody, harmony, rhythm, timbre, and even lyrics. These advancements leverage both audio and symbolic representations of music, addressing challenges like long-tail distributions and the need for interpretable results. The field's impact spans improved music information retrieval systems, enhanced music generation tools, and the development of methods to assess data replication in AI-generated music.