Lyric-Based Music Genre Classifcation
Date
2018-05-16
Authors
Yang, Junru
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
As people have access to increasingly large music data, music classifcation becomes
critical in music industry. In particular, automatic genre classifcation is an
important feature in music classi cation and has attracted much attention in recent
years. In this project report, we present our preliminary study on lyric-based music
genre classification, which uses two n-gram features to analyze lyrics of a song and
infers its genre. We use simple techniques to extract and clean the collected data. We
perform two experiments: the first generates ten top words for each of the seven music
genres under consideration, and the second classifies the test data to the seven music
genres. We test the accuracy of different classifiers, including naive bayes, linear
regression, K-nearest neighbour, decision trees, and sequential minimal optimization
(SMO). In addition, we build a website to show the results of music genre inference.
Users can also use the website to check songs that contain a specifc top word.
Description
Keywords
Music Classification, Lyrics, Text Mining, Data Mining