Deep Learning-Based Music Quality Analysis Model
In order to build an efficient and effective deep much quality recognition model, a decision fusion method by leveraging the advantages of shallow learning and deep learning is formulated. In the literature, shallow learning is a traditional music-related quality recognition method, that is, artific...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/6213115 |
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| _version_ | 1849686602232627200 |
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| author | Jing Jing |
| author_facet | Jing Jing |
| author_sort | Jing Jing |
| collection | DOAJ |
| description | In order to build an efficient and effective deep much quality recognition model, a decision fusion method by leveraging the advantages of shallow learning and deep learning is formulated. In the literature, shallow learning is a traditional music-related quality recognition method, that is, artificial statistical feature extraction and recognition are designed. Meanwhile, our deep learning module leverages the so-called PCANET network to implement the feature extraction process, and subsequently takes the spectrogram describing the music-related information as the network input. First, a variety of task classifications for the music signal problem are divided. Afterward, the optimization and adoption of deep learning in the two major problems of music feature extraction and sequence modeling are introduced. Finally, a music application is presented to illustrate the practical application of deep learning in music quality evaluation. The shallow learning features and deep learning features are seamlessly combined into the SVM model for music quality modeling, based on which differential voting mechanisms are leveraged to realize the fusion of decision-making layers. Extensive experimental results have shown that the music quality recognition rate by this method can be significantly improved on our own compiled library and the Berlin database. Besides, it exhibits obvious advantages compared with the competitors. |
| format | Article |
| id | doaj-art-c03e418e09eb435eb3613a67aff049a2 |
| institution | DOAJ |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-c03e418e09eb435eb3613a67aff049a22025-08-20T03:22:39ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/6213115Deep Learning-Based Music Quality Analysis ModelJing Jing0Music Teaching DepartmentIn order to build an efficient and effective deep much quality recognition model, a decision fusion method by leveraging the advantages of shallow learning and deep learning is formulated. In the literature, shallow learning is a traditional music-related quality recognition method, that is, artificial statistical feature extraction and recognition are designed. Meanwhile, our deep learning module leverages the so-called PCANET network to implement the feature extraction process, and subsequently takes the spectrogram describing the music-related information as the network input. First, a variety of task classifications for the music signal problem are divided. Afterward, the optimization and adoption of deep learning in the two major problems of music feature extraction and sequence modeling are introduced. Finally, a music application is presented to illustrate the practical application of deep learning in music quality evaluation. The shallow learning features and deep learning features are seamlessly combined into the SVM model for music quality modeling, based on which differential voting mechanisms are leveraged to realize the fusion of decision-making layers. Extensive experimental results have shown that the music quality recognition rate by this method can be significantly improved on our own compiled library and the Berlin database. Besides, it exhibits obvious advantages compared with the competitors.http://dx.doi.org/10.1155/2022/6213115 |
| spellingShingle | Jing Jing Deep Learning-Based Music Quality Analysis Model Applied Bionics and Biomechanics |
| title | Deep Learning-Based Music Quality Analysis Model |
| title_full | Deep Learning-Based Music Quality Analysis Model |
| title_fullStr | Deep Learning-Based Music Quality Analysis Model |
| title_full_unstemmed | Deep Learning-Based Music Quality Analysis Model |
| title_short | Deep Learning-Based Music Quality Analysis Model |
| title_sort | deep learning based music quality analysis model |
| url | http://dx.doi.org/10.1155/2022/6213115 |
| work_keys_str_mv | AT jingjing deeplearningbasedmusicqualityanalysismodel |