Music Emotion Research Based on Reinforcement Learning and Multimodal Information
Music is an important carrier of emotion and an indispensable factor in people’s daily life. With the rapid growth of digital music, people’s demand for music emotion analysis and retrieval is also increasing. With the rapid development of Internet technology, digital music has been derived continuo...
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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/10.1155/2022/2446399 |
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| _version_ | 1832545833260679168 |
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| author | Yue Hu |
| author_facet | Yue Hu |
| author_sort | Yue Hu |
| collection | DOAJ |
| description | Music is an important carrier of emotion and an indispensable factor in people’s daily life. With the rapid growth of digital music, people’s demand for music emotion analysis and retrieval is also increasing. With the rapid development of Internet technology, digital music has been derived continuously, and automatic recognition of music emotion has become the main research focus. For music, emotion is the most essential feature and the deepest inner feeling. Under the ubiquitous information environment, revealing the deep semantic information of multimodal information resources and providing users with integrated information services has important research and application value. In this paper, a multimodal fusion algorithm for music emotion analysis is proposed, and a dynamic model based on reinforcement learning is constructed to improve the analysis accuracy. The model dynamically adjusts the emotional analysis results by learning the user’s behavior, so as to realize the personalized customization of the user’s emotional preference. |
| format | Article |
| id | doaj-art-1b855258cac24fa8992d3bbff635b690 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-1b855258cac24fa8992d3bbff635b6902025-02-03T07:24:28ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/2446399Music Emotion Research Based on Reinforcement Learning and Multimodal InformationYue Hu0Shanxi Jinzhong Institute of TechnologyMusic is an important carrier of emotion and an indispensable factor in people’s daily life. With the rapid growth of digital music, people’s demand for music emotion analysis and retrieval is also increasing. With the rapid development of Internet technology, digital music has been derived continuously, and automatic recognition of music emotion has become the main research focus. For music, emotion is the most essential feature and the deepest inner feeling. Under the ubiquitous information environment, revealing the deep semantic information of multimodal information resources and providing users with integrated information services has important research and application value. In this paper, a multimodal fusion algorithm for music emotion analysis is proposed, and a dynamic model based on reinforcement learning is constructed to improve the analysis accuracy. The model dynamically adjusts the emotional analysis results by learning the user’s behavior, so as to realize the personalized customization of the user’s emotional preference.http://dx.doi.org/10.1155/2022/2446399 |
| spellingShingle | Yue Hu Music Emotion Research Based on Reinforcement Learning and Multimodal Information Journal of Mathematics |
| title | Music Emotion Research Based on Reinforcement Learning and Multimodal Information |
| title_full | Music Emotion Research Based on Reinforcement Learning and Multimodal Information |
| title_fullStr | Music Emotion Research Based on Reinforcement Learning and Multimodal Information |
| title_full_unstemmed | Music Emotion Research Based on Reinforcement Learning and Multimodal Information |
| title_short | Music Emotion Research Based on Reinforcement Learning and Multimodal Information |
| title_sort | music emotion research based on reinforcement learning and multimodal information |
| url | http://dx.doi.org/10.1155/2022/2446399 |
| work_keys_str_mv | AT yuehu musicemotionresearchbasedonreinforcementlearningandmultimodalinformation |