PENENTUAN KELAS GENRE LAGU PADA FORMAT WAV; SONGS CLASSIFICATION BASED ON GENRE IN WAV AUDIO FORMAT

Nurmiyati Tamatjita, Agus Harjoko

2013 | Disertasi | PROGRAM STUDI S2 ILMU KOMPUTER

Music genre is getting complex from time to time. As the size of digital media grows along with amount of data, manual search of digital audio files according to its genre is considered impractical and inefficient; therefore a classification mechanism is needed to improve searching. Zero Crossing Rate (ZCR), Average Energy (E) and Silent Ratio (SR) are a few of features that can be extracted from digital audio files to classify its genre. This research conducted to classify digital audio (songs) into 12 genres: Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop, Electronic, Reggae and Rock using above mentioned features, extracted from WAV audio files. Classification is performed several times using selected 3, 6, 9 and 12 genres respectively. The result shows that classification of 3 music genres (Ballad, Blues, Classic) has the highest accuracy (96.67%), followed by 6 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz) with 70%, and 9 genres (Ballad, Blues, Classic, Harmony, Hip Hop, Jazz, Keroncong, Latin, Pop) with 53.33% accuracy. Classification of all 12 music genres yields the lowest accuracy of 33.33%. The test results with the k-Nearest Neighbors algorithm to 120 songs for k = 3 accuracy reaches 22.5%, k = 5 accuracy reaches 22.5%, k = 7 accuracy reaching 26.7% and k = 9 accuracy reaches 26.7 %. So the result of determining the type of song (genre) by matching the shortest distance through the center of the class, the better the results than using the k-Nearest Neighbors (k-NN) Algorithm

Kata Kunci : Zero Crossing Rate (ZCR); Average Energy (E); Silent Ratio (SR); Euclidean Distance; Algoritma k-Nearest Neighbors (k-NN)