Utilization of genetic algorithm in classifying Filipino and Korean music through distinct windowing and perceptual features

• Main Authors: Mital, Matt Ervin G., Tobias, Rogelio Ruzcko, Bandala, Argel A., Billones, Robert Kerwin, Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2019
• Subjects: Genetic algorithms; Machine learning; Music; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/397; https://animorepository.dlsu.edu.ph/context/faculty_research/article/1396/type/native/viewcontent
• Institution: De La Salle University

Classification of songs or music in terms of genre, era and any other categories has been sought to be one of the most common yet significant research fields in digital signal processing. Usually, the aim to distinguish musical patterns is only limited to one general type (e.g., American Music). The objective of this study is to perceive the differences and similarities between two general categories namely: OPM (Original Pilipino Music), the apparent representative music of the Philippines and one of the fastest growing music industries K-POP, a general term for contemporary Korean Music. Through the features acquired from jAudio and aid of a genetic algorithm model constructed in Python with the accompaniment of the TPOT library, this research is successful in classifying the music under various settings and desired outputs. © 2019 IEEE.