Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation
Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed b...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/8752217 |
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| _version_ | 1832551474434932736 |
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| author | Lina Pan |
| author_facet | Lina Pan |
| author_sort | Lina Pan |
| collection | DOAJ |
| description | Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre-emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine-tuned by using the error back-propagation algorithm. Based on the deep belief network model after fine-tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened. |
| format | Article |
| id | doaj-art-94e2975356e64965957427f7c107a8c9 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-94e2975356e64965957427f7c107a8c92025-02-03T06:01:25ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/8752217Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse RepresentationLina Pan0School of Preschool and Art EducationAiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre-emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine-tuned by using the error back-propagation algorithm. Based on the deep belief network model after fine-tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.http://dx.doi.org/10.1155/2022/8752217 |
| spellingShingle | Lina Pan Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation Journal of Mathematics |
| title | Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation |
| title_full | Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation |
| title_fullStr | Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation |
| title_full_unstemmed | Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation |
| title_short | Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation |
| title_sort | automatic classification method of music genres based on deep belief network and sparse representation |
| url | http://dx.doi.org/10.1155/2022/8752217 |
| work_keys_str_mv | AT linapan automaticclassificationmethodofmusicgenresbasedondeepbeliefnetworkandsparserepresentation |