Using self-organizing maps to cluster music files based on lyrics and audio features
Date of Publication
2013
Document Type
Master's Thesis
Degree Name
Master of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Arnulfo Azcarraga
Abstract/Summary
Music is an integral part of our everyday lives, and with the advent of portable music players with large storage capacities to maintain large archives of songs, users need to be able to organize their archives in a manner that would allow them to retrieve the songs that they want to listen. It would also be useful for he archive to be able to recommend songs related to the song that has just been played, as a new way of accessing music archives. This research studies the effect of clustering music files using both audio features and song lyrics. Audio features refer to the melody, pitch and other audible features of the song, whereas song lyrics refer to the words used in the song. Clusters of these music files produced by Self-Organizing Maps (SOM) are compared with the natural groupings of the songs based on their actual genre. This research shows that using lyrics alone to cluster music files is not enough. However, when the lyrics and audio are used at the same time, or when audio is used to refine the clusters already made by lyrics, a more organized SOM can be produced.
Abstract Format
html
Language
English
Format
Accession Number
TG05330
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
xi, 94 leaves ; 28 cm. + 1 computer optical disc.
Keywords
Music; Tone clusters; Self-organizing maps
Recommended Citation
Enriquez, C. (2013). Using self-organizing maps to cluster music files based on lyrics and audio features. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/4368