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  1. Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies (MIRUM '11)
  2. Music genre classification using explicit semantic analysis
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The need for music information retrieval with user-centered and multimodal strategies
Audiovisual archive exploitation in the networked information society
Music identification via vocabulary tree with MFCC peaks
Analyzing sound tracings: a multimodal approach to music information retrieval
Affective content analysis of music video clips
Finding geographically representative music via social media
Advantages of nonstationary gabor transforms in beat tacking
A tempo-sensitive music search engine with multimodal inputs
Music genre classification using explicit semantic analysis
A musical mood trajectory estimation method using lyrics and acoustic features
What is a "Musical World"? An affinity propagation approach

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Music genre classification using explicit semantic analysis

Content Provider ACM Digital Library
Author Shokoufandeh, Ali Aryafar, Kamelia
Abstract Music genre classification is the categorization of a piece of music into its corresponding categorical labels created by humans and has been traditionally performed through a manual process. Automatic music genre classification, a fundamental problem in the musical information retrieval community, has been gaining more attention with advances in the development of the digital music industry. Most current genre classification methods tend to be based on the extraction of short-time features in combination with high-level audio features to perform genre classification. However, the representation of short-time features, using time windows, in a semantic space has received little attention. This paper proposes a vector space model of mel-frequency cepstral coefficients (MFCCs) that can, in turn, be used by a supervised learning schema for music genre classification. Inspired by explicit semantic analysis of textual documents using term frequency-inverse document frequency (tf-idf), a semantic space model is proposed to represent music samples. The effectiveness of this representation of audio samples is then demonstrated in music genre classification using various machine learning classification algorithms, including support vector machines (SVMs) and k-nearest neighbor clustering. Our preliminary results suggest that the proposed method is comparable to genre classification methods that use low-level audio features.
Starting Page 33
Ending Page 38
Page Count 6
File Format PDF
ISBN 9781450309868
DOI 10.1145/2072529.2072539
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-11-30
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Audio word Music genre classification Vocabulary Explicit semantic analysis
Content Type Text
Resource Type Article
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