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| Identifier |
uch.csd.msc//2006holzapfel |
| Title |
Μία Προσέγγιση Ταξινόμησης Μουσικής βασισμένη σε Συνιστώσες |
| Alternative Title |
A Component Based Music Classification Approach |
| Creator |
Holzapfel, Andre
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| Abstract |
This thesis introduces a new feature set based on a Non-negative Matrix Factorization approach for the classification of musical signals into genres, only using synchronous organization of music events (vertical dimension of music). This feature set generates a vector space to describe the spectrogram representation of a music signal. The space is modeled statistically by a mixture of Gaussians (GMM). A new signal is classified by considering the likelihoods over all the estimated feature vectors given these statistical models, without constructing a model for the signal itself. Cross-validation tests on two commonly utilized datasets for this task show the superiority of the proposed features compared to the widely used MFCC type of representation based on classification accuracies (over 9% of improvement), as well as on a stability measure introduced in this thesis for GMM. Furthermore, we compare results of Non-negative Matrix Factorization and Independent Component Analysis when used for the approximation of spectrograms, documenting the superiority of Non-negative Matrix Factorization. Based on our findings we give a concept for a complete musical genre classification system using matrix factorization and Support Vector Machines.
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| Issue date |
2006-12-01 |
| Date available |
2006-12-08 |
| Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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| Views |
426 |
Μία Προσέγγιση Ταξινόμησης Μουσικής βασισμένη σε Συνιστώσες
