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PresentationOpen Access (License Unspecified), Open Access
Conference presentation
Automatic Classification of Digital Music by Genre
26 Feb 2013
Abstract
Over the past two decades, advances in the digital music industry have resulted in an exponential growth in music data sets. This exponential growth has in turn spurred great interest in music information retrieval (MIR) problems, organizing large music collections, and content-based search methods for digital music libraries. Equally important are the related problems in music classification such as genre classification, music mood analysis, and artist identification. Music genre classification is a well-studied problem in the music information retrieval community and has a wide range of applications. In this project we address the problem of genre classification by representing the MFCC feature vectors in an extended semantic space. We combine this audio representation with machine learning techniques to perform genre classification with the goal of obtaining higher classification accuracy.
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Details
- Title
- Automatic Classification of Digital Music by Genre
- Creators
- Kamelia Aryafar - Drexel UniversityAli Shokoufandeh - Drexel University
- Conference
- Grace Hopper Celebration of Women in Computing (GHC'12) (Baltimore, Maryland, 2012)
- Resource Type
- Conference presentation
- Language
- English
- Academic Unit
- Computer Science; College of Engineering
- Other Identifier
- 991014632944704721