Piano Playing Gesture Recognition Based on Multiple Intelligences Theory
In this paper, firstly, in order to solve the piano playing recognition problem in the field of artificial intelligence, based on the theory of multiple intelligences, the VGG-16 deep network migration learning algorithm is applied to estimate and acquire the piano playing gesture posture. Secondly,...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.01230 |
| _version_ | 1797340632010719232 |
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| author | Zhang Jiahui |
| author_facet | Zhang Jiahui |
| author_sort | Zhang Jiahui |
| collection | DOAJ |
| description | In this paper, firstly, in order to solve the piano playing recognition problem in the field of artificial intelligence, based on the theory of multiple intelligences, the VGG-16 deep network migration learning algorithm is applied to estimate and acquire the piano playing gesture posture. Secondly, combined with the Iterative Update Extended Kalman Filter (IUEKF) algorithm, the micro-inertial sensor fixation of the piano-playing gesture is realized, which in turn is conducive to improving the piano-playing gesture recognition accuracy. Then, we obtain real-time piano-playing gesture information through a Kinect somatosensory device, construct a piano-playing gesture recognition model based on migration learning on the basis of obtaining piano-playing gesture features, and confirm the effectiveness of the model through the experimental study of piano-playing recognition. The results show that in piano-playing gesture recognition, the recognition accuracy of this paper’s method remains above 0.9, and the application of this paper’s method can effectively improve the recognition accuracy of piano-playing gestures. On piano playing pedal action recognition, this paper’s method shows that the average F-measure scores of these two strategies are 0.924 and 0.944, respectively, which are better compared to other methods. This study provides an effective case for applying AI techniques to piano performance recognition and broadens the scope of AI applications. |
| first_indexed | 2024-03-08T10:05:57Z |
| format | Article |
| id | doaj.art-a7a83f4053ee46fb905002c1aad75f88 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:05:57Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-a7a83f4053ee46fb905002c1aad75f882024-01-29T08:52:40ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.01230Piano Playing Gesture Recognition Based on Multiple Intelligences TheoryZhang Jiahui01School of Music, Shandong University of Technology, Zibo, Shandong, 255000, China.In this paper, firstly, in order to solve the piano playing recognition problem in the field of artificial intelligence, based on the theory of multiple intelligences, the VGG-16 deep network migration learning algorithm is applied to estimate and acquire the piano playing gesture posture. Secondly, combined with the Iterative Update Extended Kalman Filter (IUEKF) algorithm, the micro-inertial sensor fixation of the piano-playing gesture is realized, which in turn is conducive to improving the piano-playing gesture recognition accuracy. Then, we obtain real-time piano-playing gesture information through a Kinect somatosensory device, construct a piano-playing gesture recognition model based on migration learning on the basis of obtaining piano-playing gesture features, and confirm the effectiveness of the model through the experimental study of piano-playing recognition. The results show that in piano-playing gesture recognition, the recognition accuracy of this paper’s method remains above 0.9, and the application of this paper’s method can effectively improve the recognition accuracy of piano-playing gestures. On piano playing pedal action recognition, this paper’s method shows that the average F-measure scores of these two strategies are 0.924 and 0.944, respectively, which are better compared to other methods. This study provides an effective case for applying AI techniques to piano performance recognition and broadens the scope of AI applications.https://doi.org/10.2478/amns.2023.2.01230multiple intelligence theoryvgg-16 deep networktransfer learning algorithmpiano playinggesture recognition97r40 |
| spellingShingle | Zhang Jiahui Piano Playing Gesture Recognition Based on Multiple Intelligences Theory Applied Mathematics and Nonlinear Sciences multiple intelligence theory vgg-16 deep network transfer learning algorithm piano playing gesture recognition 97r40 |
| title | Piano Playing Gesture Recognition Based on Multiple Intelligences Theory |
| title_full | Piano Playing Gesture Recognition Based on Multiple Intelligences Theory |
| title_fullStr | Piano Playing Gesture Recognition Based on Multiple Intelligences Theory |
| title_full_unstemmed | Piano Playing Gesture Recognition Based on Multiple Intelligences Theory |
| title_short | Piano Playing Gesture Recognition Based on Multiple Intelligences Theory |
| title_sort | piano playing gesture recognition based on multiple intelligences theory |
| topic | multiple intelligence theory vgg-16 deep network transfer learning algorithm piano playing gesture recognition 97r40 |
| url | https://doi.org/10.2478/amns.2023.2.01230 |
| work_keys_str_mv | AT zhangjiahui pianoplayinggesturerecognitionbasedonmultipleintelligencestheory |