Feature Extraction and Classification of Music Content Based on Deep Learning
To study the use of in-depth training in extracting and classifying the content of music samples, the work offers an algorithm for identifying and classifying musical genres based on a deep network of beliefs, enabling it to be used to extract and classify traditional Chinese musical instruments, an...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/8320808 |
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| _version_ | 1832551210865917952 |
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| author | Qianqiu Shi Young Chun Ko |
| author_facet | Qianqiu Shi Young Chun Ko |
| author_sort | Qianqiu Shi |
| collection | DOAJ |
| description | To study the use of in-depth training in extracting and classifying the content of music samples, the work offers an algorithm for identifying and classifying musical genres based on a deep network of beliefs, enabling it to be used to extract and classify traditional Chinese musical instruments, and using real-world experiments to test its performance after training. The experimental results are as follows: the improved depth confidence network algorithm has the highest accuracy for music recognition and classification, which can reach 75.8%, higher than other traditional methods. The improved depth confidence network identifies and classifies Chinese traditional musical instruments through Softmax layer, and the accuracy is even as high as 99.2%; DBN is combined with Softmax neural network algorithm when only a few labeled samples in the training set are used for network fine-tuning, and the accuracy of the algorithm can still reach more than 90%, which can reduce the workload in the early stage. This study effectively solves the problem of too much workload and low accuracy in the process of music content recognition, classification, and extraction. |
| format | Article |
| id | doaj-art-b90ecf3c5460484ab7bfc5c346be4dbe |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-b90ecf3c5460484ab7bfc5c346be4dbe2025-02-03T06:04:44ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/8320808Feature Extraction and Classification of Music Content Based on Deep LearningQianqiu Shi0Young Chun Ko1Academy of MusicDepartment of Teaching ProfessionTo study the use of in-depth training in extracting and classifying the content of music samples, the work offers an algorithm for identifying and classifying musical genres based on a deep network of beliefs, enabling it to be used to extract and classify traditional Chinese musical instruments, and using real-world experiments to test its performance after training. The experimental results are as follows: the improved depth confidence network algorithm has the highest accuracy for music recognition and classification, which can reach 75.8%, higher than other traditional methods. The improved depth confidence network identifies and classifies Chinese traditional musical instruments through Softmax layer, and the accuracy is even as high as 99.2%; DBN is combined with Softmax neural network algorithm when only a few labeled samples in the training set are used for network fine-tuning, and the accuracy of the algorithm can still reach more than 90%, which can reduce the workload in the early stage. This study effectively solves the problem of too much workload and low accuracy in the process of music content recognition, classification, and extraction.http://dx.doi.org/10.1155/2022/8320808 |
| spellingShingle | Qianqiu Shi Young Chun Ko Feature Extraction and Classification of Music Content Based on Deep Learning Advances in Multimedia |
| title | Feature Extraction and Classification of Music Content Based on Deep Learning |
| title_full | Feature Extraction and Classification of Music Content Based on Deep Learning |
| title_fullStr | Feature Extraction and Classification of Music Content Based on Deep Learning |
| title_full_unstemmed | Feature Extraction and Classification of Music Content Based on Deep Learning |
| title_short | Feature Extraction and Classification of Music Content Based on Deep Learning |
| title_sort | feature extraction and classification of music content based on deep learning |
| url | http://dx.doi.org/10.1155/2022/8320808 |
| work_keys_str_mv | AT qianqiushi featureextractionandclassificationofmusiccontentbasedondeeplearning AT youngchunko featureextractionandclassificationofmusiccontentbasedondeeplearning |