Symbolic music genre classification based on note pitch and duration

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4152 LNCS, Issue , 2006, Pages 329-338

  Karydis, Ioannis ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Content based information retrieval; Duration; Histograms; Music features; Music genre classification; Pitch

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CODES (SYMBOLS); COMPUTER SCIENCE;

CONTENT BASED INFORMATION RETRIEVAL; DURATION; HISTOGRAMS; MUSIC FEATURES; MUSIC GENRE CLASSIFICATION; PITCH;

COMPUTER MUSIC;

EID: 33750052904     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11827252_25     Document Type: Conference Paper

References (6)

1. R. Basili, A. Serafini, and A. Stellato. Classification of musical genre: a machine learning approach. In Proceedings of ISMIR, 2004.
2. D. Byrd and T. Crawford. Problems of music information retrieval in the real world. Information Processing and Management, 38(2):249-272, 2002.
3. C. McKay and I. Fujinaga. Automatic genre classification using large high-level musical feature sets. In Proceedings of ISMIR, pages 31-38, 2004.
4. A. C. North and D. J. Hargreaves. Liking for musical styles. Musicae Scientiae, 1:109-128, 1997.
5. G. Tzanetakis, A. Ermolinskyi, and P. Cook. Pitch histograms in audio and symbolic music information retrieval. In Proceedings of ISMIR, pages 31-38, 2002.
6. The Humdrum website. A library of virtual musical scores in the humdrum **kern data format, http://kern.humdrum.net.