Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition
We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone ph...
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| Vydáno v: | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) s. 2661 - 2665 |
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| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
01.04.2018
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| ISSN: | 2379-190X |
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| Abstract | We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no longer organized in a waterfall scheme. The whole system is realized by traversing backtrack arrows and cycles between various stages. In order to give a compact and effective representation of the features, the standard early temporal integration is combined with other selection and extraction phases: on the one hand, the selection of the most meaningful characteristics based on information gain, and on the other hand, the inclusion of the nonlinear correlation between this subset of features, determined by an autoencoder. The results of the experiments conducted on GTZAN dataset reveal a noticeable contribution of this methodology towards the model's performance in classification task. |
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| AbstractList | We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no longer organized in a waterfall scheme. The whole system is realized by traversing backtrack arrows and cycles between various stages. In order to give a compact and effective representation of the features, the standard early temporal integration is combined with other selection and extraction phases: on the one hand, the selection of the most meaningful characteristics based on information gain, and on the other hand, the inclusion of the nonlinear correlation between this subset of features, determined by an autoencoder. The results of the experiments conducted on GTZAN dataset reveal a noticeable contribution of this methodology towards the model's performance in classification task. |
| Author | Tibo, Alessandro Bientinesi, Paolo Raissi, Tina |
| Author_xml | – sequence: 1 givenname: Tina surname: Raissi fullname: Raissi, Tina organization: RWTH Aachen University, AICES, Schinkelstr. 2, Aachen, 52062, Germany – sequence: 2 givenname: Alessandro surname: Tibo fullname: Tibo, Alessandro organization: Department of Information Engineering, University of Florence, Via S. Marta 3, Firenze, 50139, Italy – sequence: 3 givenname: Paolo surname: Bientinesi fullname: Bientinesi, Paolo |
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| Snippet | We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical... |
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| SubjectTerms | autoencoder Feature extraction feature extraction and selection genre classification information gain Mel frequency cepstral coefficient Music Musical signal Pipelines Task analysis Training |
| Title | Extended Pipeline for Content-Based Feature Engineering in Music Genre Recognition |
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