Lyric-Based Music Genre Classifcation
| dc.contributor.author | Yang, Junru | |
| dc.contributor.supervisor | Wu, Kui | |
| dc.contributor.supervisor | Tzanetakis, George | |
| dc.date.accessioned | 2018-05-17T04:11:02Z | |
| dc.date.available | 2018-05-17T04:11:02Z | |
| dc.date.copyright | 2018 | en_US |
| dc.date.issued | 2018-05-16 | |
| dc.degree.department | Department of Computer Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.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. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/9378 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Music Classification | en_US |
| dc.subject | Lyrics | en_US |
| dc.subject | Text Mining | en_US |
| dc.subject | Data Mining | en_US |
| dc.title | Lyric-Based Music Genre Classifcation | en_US |
| dc.type | project | en_US |