Yoppy, Sazaki and Adib, Aramadhan (2014) Rock Genre Classification Using K-Nearest Neighbor. In: 1st International Conference on Computer Science and Engineering, 1 - 2 Oktober 2014, Palembang.
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Abstract
Music genre classification is a part of Music Information Retrieval. This research was a genre music detection based on signal from an audio. Divided into two processes namely extraction of features and classification. Signal would be transformed using Fast Fourier Transform to get frequency domain signal which will be processed to extract Short Time Energy, Spectral Centroid, Spectral Roll-Off, Spectral Flux, and Energy Entropy feature. Besides those features, Zero Crossing Rate would be counted from time-domain signal. in classifying phase, research using k nearest neighbor with accuracy reaching 54,44%
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | T Technology > T Technology (General) > T58.4 Managerial control systems Information technology. Information systems (General) |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | Mr Yoppy Sazaki |
| Date Deposited: | 01 Jan 2020 08:58 |
| Last Modified: | 01 Jan 2020 08:58 |
| URI: | http://repository.unsri.ac.id/id/eprint/22732 |
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