A Survey of Audio-Based Music Classification and Annotation

• Published in: IEEE Transactions on Multimedia Vol. 13; no. 2; pp. 303 - 319
• Main Authors: Fu, Zhouyu, Lu, Guojun, Ting, Kai Ming, Zhang, Dengsheng
• Format: Book Review Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic signal processing; Acoustics; Annotations; Applied sciences; Artists; Classification; classification algorithms; Communities; Computer science; control theory; systems; Exact sciences and technology; Feature extraction; Fundamental areas of phenomenology (including applications); Information retrieval; Information systems. Data bases; Instruments; Memory organisation. Data processing; Multimedia; Music; Music and musical intruments; Music industry; music information retrieval; Physics; Recognition; Semantics; Software; State of the art; Studies; Tasks; Taxonomy; Timbre; Artist; classification algorithms; Database query; Acoustic signal; Very large databases; Information retrieval; Emotion emotionality; Pattern recognition; Acoustic signal processing; Annotation; Stress; Mood; Music; Musical acoustics; Classification; Signal processing; Audio acoustics; Feature extraction; Pattern extraction; music information retrieval
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2010.2098858

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.