Very short feature vector for music genre classiciation based on distance metric lerning

In our study, a very short feature vector, obtained from low dimensional projection and already developed audio features, is used for music genre classification problem. A long feature vector based on the concatenation of various features is generally used in music genre classification system. Our o...

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Bibliographic Details
Published in2014 International Conference on Audio, Language and Image Processing pp. 726 - 729
Main Authors Dalwon Jang, Sei-Jin Jang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2014
Subjects
Accuracy
Classification algorithms
dimension reduction
distance metric learning
Feature extraction
Measurement
Mel frequency cepstral coefficient
music genre classification
Principal component analysis
Support vector machine classification
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Summary:In our study, a very short feature vector, obtained from low dimensional projection and already developed audio features, is used for music genre classification problem. A long feature vector based on the concatenation of various features is generally used in music genre classification system. Our objective is to find a short feature vector, and we applied a distance metric learning algorithm in order to reduce the dimensionality of feature vector with a little performance degradation. In our experiments based on two widely-used dataset, dimension reduction based on distance metric learning is very effective, and we can get over 80% of accuracy with only 10-dimensional feature vector.
ISBN:9781479939022
1479939021
DOI:10.1109/ICALIP.2014.7009890