Comparer des méthodes

	Mesure de similarité musicale	Classification de genres musicaux
Domaine	Recherche d'information musicale	Recherche d'information musicale
Famille	Machine learning	Machine learning
Année d'origine	2001	2002
Auteur d'origine	Beth Logan	George Tzanetakis
Type	Content-based audio similarity	Audio feature-based classification
Source fondatrice	Logan, B., & Salomon, A. (2001). A music similarity function based on song structure. In Proceedings of the International Conference on Music Information Retrieval.	Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302.
Alias	music distance metric, timbral similarity, content-based similarity	genre recognition, music categorization, style classification
Apparentées	5	5
Résumé	Music similarity measures are computational methods for assessing how musically related two audio recordings are. Introduced by Logan (2001), similarity measures enable content-based music recommendation, playlist generation, and music discovery. Unlike fingerprinting, which identifies the same song, similarity measures gauge stylistic, timbral, and structural resemblance between different songs. Measures can be acoustic (comparing spectral features), high-level (genre, mood), or hybrid.	Music genre classification is the task of automatically assigning genre labels (rock, jazz, classical, pop, etc.) to audio recordings. Introduced formally by Tzanetakis and Cook (2002), it is one of the earliest and most studied music information retrieval problems. It remains critical for music discovery, recommendation systems, digital library organization, and music streaming services. Modern systems achieve high accuracy on standard datasets using deep learning.