Machine Learning Approaches for Music Information Retrieval

The rapid growth of the Internet and the advancements of Internet technologies have made it possible for music listeners to have access to a large amount of on-line music data, including music sound signals, lyrics, biographies, and discographies. Music artists in the 21st century are promoted through various kinds of websites that are managed by themselves, by their fans, or by their record companies. Also, they are subjects of discussions in Internet newsgroups and bulletin boards. This raises the question of whether computer programs can enrich the experience of music listeners by enabling the listeners to have access to such a large volume of on-line music data. Multimedia conferences, e.g. ISMIR (International Conference on Music Information Retrieval) and WEDELMUSIC (Web Delivery of Music), have a focus on the development of computational techniques for analyzing, summarizing, indexing, and classifying music data. In [Huron, 2000] Huron points out that since the preeminent functions of music are social and psychological, the most useful characterizationwould be based on four types of information:

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