Muda, N. A. (2010) MODIFIED AIS-BASED CLASSIFIER FOR MUSIC GENRE CLASSIFICATION. In: 11th International Society for Music Information Retrieval Conference, 9 - 13 August 2010, Utrecht, Netherlands.
![[img]](http://eprints.utem.edu.my/style/images/fileicons/application_pdf.png) |
PDF
Modified_AIS-Based_Classifier_for_Music_Genre_Classification.pdf - Published Version Restricted to Registered users only Download (1MB) |
Abstract
Automating human capabilities for classifying different genre of songs is a difficult task. This has led to various studies that focused on finding solutions to solve this problem. Analyzing music contents (often referred as content- based analysis) is one of many ways to identify and group similar songs together. Various music contents, for example beat, pitch, timbral and many others were used and analyzed to represent the music. To be able to manipulate these content representations for recognition: feature extraction and classification are two major focuses of investigation in this area. Though various classification techniques proposed so far, we are introducing yet another one. The objective of this paper is to introduce a possible new technique in the Artificial Immune System (AIS) domain called a modified immune classifier (MIC) for music genre classification. MIC is the newest version of Negative Selection Algorithm (NSA) where it stresses the self and non-self cells recognition and a complementary process for generating detectors. The discussion will detail out the MIC procedures applied and the modified part in solving the classification problem. At the end, the results of proposed framework will be presented, discussed and directions for future work are given.
| Item Type: | Conference or Workshop Item (Poster) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Faculty of Information and Communication Technology > Department of Software Engineeering |
| Depositing User: | Ms Noor Azilah Muda |
| Date Deposited: | 17 Nov 2011 10:07 |
| Last Modified: | 28 May 2015 02:17 |
| URI: | http://eprints.utem.edu.my/id/eprint/156 |
| Statistic Details: | View Download Statistic |
Actions (login required)
 |
View Item |