NDLI: Genre classification of symbolic music with SMBGT

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Genre classification of symbolic music with SMBGT

Content Provider	ACM Digital Library
Author	Kotsifakos, Evangelos E. Athitsos, Vassilis Papapetrou, Panagiotis Kotsifakos, Alexios
Abstract	Automatic music genre classification is a task that has attracted the interest of the music community for more than two decades. Music can be of high importance within the area of assistive technologies as it can be seen as an assistive technology with high therapeutic and educational functionality for children and adults with disabilities. Several similarity methods and machine learning techniques have been applied in the literature to deal with music genre classification, and as a result data mining and Music Information Retrieval (MIR) are strongly interconnected. In this paper, we deal with music genre classification for symbolic music, and specifically MIDI, by combining the recently proposed novel similarity measure for sequences, SMBGT, with the k-Nearest Neighbor (k-NN) classifier. For all MIDI songs we first extract all of their channels and then transform each channel into a sequence of 2D points, providing information for pitch and duration of their music notes. The similarity between two songs is found by computing the SMBGT for all pairs of the songs' channels and getting the maximum pairwise channel score as their similarity. Each song is treated as a query to which k-NN is applied, and the returned genre of the classifier is the one with the majority of votes in the k neighbors. Classification accuracy results indicate that there is room for improvement, especially due to the ambiguous definitions of music genres that make it hard to clearly discriminate them. Using this framework can also help us analyze and understand potential disadvantages of SMBGT, and thus identify how it can be improved when used for classification of real-time sequences.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450319737
DOI	10.1145/2504335.2504382
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-05-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Smbgt Midi Classification Genre Sequence
Content Type	Text
Resource Type	Article

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