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Audio feature reduction and analysis for automatic music genre classification | IEEE Conference Publication | IEEE Xplore

Audio feature reduction and analysis for automatic music genre classification


Abstract:

Multimedia database retrieval is growing at a fast rate thereby subsequent increase in the popularity of online retrieval system. The large datasets are major challenges ...Show More

Abstract:

Multimedia database retrieval is growing at a fast rate thereby subsequent increase in the popularity of online retrieval system. The large datasets are major challenges for searching, retrieving, and organizing the music content. Therefore, there is a need of robust automatic music genre classification method for organizing these music data into different classes according to the certain viable information. There are two fundamental components to be considered for genre classification namely audio feature extraction and classifier design. In this paper, diverse audio features set have been proposed to characterize the music contents precisely. The feature sets belong to four different groups, i.e. dynamic, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as representatives, including up to the 4th order central moments of each feature, and covariance components. Ultimately, significant numbers of representative attributes are controlled by MRMR algorithm. The algorithm calculates the score level of all feature attributes and orders them. The high score feature attributes are only considered for genre classification. Moreover, we can visualize that which audio features and which of the different statistical parameters derived from them are important for genre classification. Among them, mel frequency cepstral coefficients (MFCCs) have higher scored level than other feature attributes. Furthermore, MRMR does not transform the feature value like as principal component analysis (PCA). Besides these, the comparison has been made based on classification accuracy between two-dimensionality reduction methodologies using support vector machine (SVM). The classification accuracy of MRMR feature reduction set outperforms than PCA. The overall classification is also higher than other existing state-of-the-art of frame base methods.
Date of Conference: 05-08 October 2014
Date Added to IEEE Xplore: 04 December 2014
Electronic ISBN:978-1-4799-3840-7
Print ISSN: 1062-922X
Conference Location: San Diego, CA, USA

References

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