Automatic music genre classification system
Date of Publication
2008
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Nelson Marcos
Defense Panel Member
Joel Ilao
Rafael Cabredo
Abstract/Summary
To address the problems of manual classification, the proponents created a system that will automatically classify music into their genres. The genres considered are blues, classical, hip hop, jazz, pop and rock. The system compares different feature extraction to come up with good algorithm contributions. The system makes represent make use feature extraction algorithms to compute for the feature vectors, which represent the data to be classified. The features used are timbre, rhythm and pitch. K-Nearest Neighbor which is the classification algorithm used, utilize these feature vectors to compute for decision boundaries that separate each genre and classify the music to its corresponding genre. The system achieved 69.42% using timbre, 41.25% using rhythm, 32.75% using pitch and 72.83% using the combination of the three features and K = 6. Blues and classical songs were classified more accurately than other genres.
Abstract Format
html
Language
English
Format
Accession Number
TU14647
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Recommended Citation
Ong, J. L., Po, C. T., & Siy, J. O. (2008). Automatic music genre classification system. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/10926
