Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal
Music classification is conducive to online music retrieval, but the current music classification model finds it difficult to accurately identify various types of music, which makes the classification effect of the current music classification model poor. In order to improve the accuracy of music cl...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5577486 |
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| _version_ | 1832558951833534464 |
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| author | Bo Sun |
| author_facet | Bo Sun |
| author_sort | Bo Sun |
| collection | DOAJ |
| description | Music classification is conducive to online music retrieval, but the current music classification model finds it difficult to accurately identify various types of music, which makes the classification effect of the current music classification model poor. In order to improve the accuracy of music classification, a music classification model based on multifeature fusion and machine learning algorithm is proposed. First, we obtain the music signal, and then extract various features from the classification of the music signal, and use machine learning algorithms to describe the type of music signal and the relationship between the features. The music classifier and deep belief network machine learning models in shallow logistic regression are established, respectively. Experiments were designed for these two models to verify the applicability of the model for music classification. By comparing the experimental results, it is found that the classification accuracy of the deep confidence network model is higher than that of the logistic regression model, but the number of iterations needed for its accuracy to converge is also higher than that of the logistic regression model. Compared with other current music classification models, this model reduces the time of constructing music classifier, speeds up the speed of music classification, and can identify various types of music with high precision. The accuracy of music classification is obviously improved, which verifies the superiority of this music classification model. |
| format | Article |
| id | doaj-art-af13b75d5b8142f7922bd86eaa20f444 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-af13b75d5b8142f7922bd86eaa20f4442025-02-03T01:31:13ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55774865577486Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music SignalBo Sun0School of Arts, Hunan City College, Yiyang, Hunan 413000, ChinaMusic classification is conducive to online music retrieval, but the current music classification model finds it difficult to accurately identify various types of music, which makes the classification effect of the current music classification model poor. In order to improve the accuracy of music classification, a music classification model based on multifeature fusion and machine learning algorithm is proposed. First, we obtain the music signal, and then extract various features from the classification of the music signal, and use machine learning algorithms to describe the type of music signal and the relationship between the features. The music classifier and deep belief network machine learning models in shallow logistic regression are established, respectively. Experiments were designed for these two models to verify the applicability of the model for music classification. By comparing the experimental results, it is found that the classification accuracy of the deep confidence network model is higher than that of the logistic regression model, but the number of iterations needed for its accuracy to converge is also higher than that of the logistic regression model. Compared with other current music classification models, this model reduces the time of constructing music classifier, speeds up the speed of music classification, and can identify various types of music with high precision. The accuracy of music classification is obviously improved, which verifies the superiority of this music classification model.http://dx.doi.org/10.1155/2021/5577486 |
| spellingShingle | Bo Sun Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal Complexity |
| title | Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal |
| title_full | Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal |
| title_fullStr | Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal |
| title_full_unstemmed | Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal |
| title_short | Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal |
| title_sort | using machine learning algorithm to describe the connection between the types and characteristics of music signal |
| url | http://dx.doi.org/10.1155/2021/5577486 |
| work_keys_str_mv | AT bosun usingmachinelearningalgorithmtodescribetheconnectionbetweenthetypesandcharacteristicsofmusicsignal |