Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure
To improve the accuracy of music segmentation and enhance segmentation effect, an algorithm based on the adaptive update of confidence measure is proposed. According to the theory of compressed sensing, the music fragments are denoised, and thus the denoised signals are subjected to short-term corre...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/8329088 |
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| _version_ | 1832560939058069504 |
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| author | Jianhua Li |
| author_facet | Jianhua Li |
| author_sort | Jianhua Li |
| collection | DOAJ |
| description | To improve the accuracy of music segmentation and enhance segmentation effect, an algorithm based on the adaptive update of confidence measure is proposed. According to the theory of compressed sensing, the music fragments are denoised, and thus the denoised signals are subjected to short-term correlation analysis. Then, the pitch frequency is extracted, and the music fragments are roughly classified by wavelet transform to realize the preprocessing of the music fragments. In order to calculate the confidence measure of the music segment, the SVM method is used, whereas the adaptive update of the confidence measure is studied using reliable data selection algorithm. The dynamic threshold notes are segmented according to the update result to realize music segmentation. Experimental results show that the recall and precision values of the algorithm reach 97.5% and 93.8%, respectively, the segmentation error rate is low, and it can achieve effective segmentation of music fragments, indicating that the algorithm is effective. |
| format | Article |
| id | doaj-art-3f6ead73fe2a4ee1b856916ce3585b00 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-3f6ead73fe2a4ee1b856916ce3585b002025-02-03T01:26:24ZengWileyJournal of Mathematics2314-47852021-01-01202110.1155/2021/8329088Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence MeasureJianhua Li0School of Music and DanceTo improve the accuracy of music segmentation and enhance segmentation effect, an algorithm based on the adaptive update of confidence measure is proposed. According to the theory of compressed sensing, the music fragments are denoised, and thus the denoised signals are subjected to short-term correlation analysis. Then, the pitch frequency is extracted, and the music fragments are roughly classified by wavelet transform to realize the preprocessing of the music fragments. In order to calculate the confidence measure of the music segment, the SVM method is used, whereas the adaptive update of the confidence measure is studied using reliable data selection algorithm. The dynamic threshold notes are segmented according to the update result to realize music segmentation. Experimental results show that the recall and precision values of the algorithm reach 97.5% and 93.8%, respectively, the segmentation error rate is low, and it can achieve effective segmentation of music fragments, indicating that the algorithm is effective.http://dx.doi.org/10.1155/2021/8329088 |
| spellingShingle | Jianhua Li Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure Journal of Mathematics |
| title | Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure |
| title_full | Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure |
| title_fullStr | Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure |
| title_full_unstemmed | Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure |
| title_short | Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure |
| title_sort | music segmentation algorithm based on self adaptive update of confidence measure |
| url | http://dx.doi.org/10.1155/2021/8329088 |
| work_keys_str_mv | AT jianhuali musicsegmentationalgorithmbasedonselfadaptiveupdateofconfidencemeasure |