Music source feature extraction based on improved attention mechanism and phase feature
Music source feature extraction is an important research direction in music information retrieval and music recommendation system. To extract the features of music sources more effectively, the study introduces the jump attention mechanism and combines it with the convolutional attention module. Als...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000784 |
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| _version_ | 1850132236751339520 |
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| author | Weina Yu |
| author_facet | Weina Yu |
| author_sort | Weina Yu |
| collection | DOAJ |
| description | Music source feature extraction is an important research direction in music information retrieval and music recommendation system. To extract the features of music sources more effectively, the study introduces the jump attention mechanism and combines it with the convolutional attention module. Also, a feature extraction module based on Unet + + and spatial attention module is proposed. In addition, the phase feature information of the mixed music signals is utilized to improve the network performance. Results showed that this model was studied to perform well in music source separation experiments of vocals and accompaniment. For vocal separation on the MIR-1K dataset, the model achieves 11.25 dB, 17.34 dB, and 13.83 dB for each metric, respectively. Meanwhile, for drum separation on the DSD100 dataset, the model achieves a median signal-to-source distortion ratio of 4.36 dB, which is 2.91 dB better than that of the Spectral Hierarchical Network model. For the separation of the bass sound and the human voice, the model's in the separation of bass and human voice, the median distortion ratio of the model is as high as 4.87 dB and 6.09 dB, which is better than that of the Spectral Hierarchical Network model. This indicates the significant performance advantages in feature extraction and separation of music sources, and it has important application values in music production and speech recognition. |
| format | Article |
| id | doaj-art-0719fe365b9e4be282f9944862c36b95 |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-0719fe365b9e4be282f9944862c36b952025-08-20T02:32:15ZengElsevierSystems and Soft Computing2772-94192024-12-01620014910.1016/j.sasc.2024.200149Music source feature extraction based on improved attention mechanism and phase featureWeina Yu0Music and Dance Post-doctoral Research Mobile Station, Nanjing University of the Arts, Nan'jing 210013, ChinaMusic source feature extraction is an important research direction in music information retrieval and music recommendation system. To extract the features of music sources more effectively, the study introduces the jump attention mechanism and combines it with the convolutional attention module. Also, a feature extraction module based on Unet + + and spatial attention module is proposed. In addition, the phase feature information of the mixed music signals is utilized to improve the network performance. Results showed that this model was studied to perform well in music source separation experiments of vocals and accompaniment. For vocal separation on the MIR-1K dataset, the model achieves 11.25 dB, 17.34 dB, and 13.83 dB for each metric, respectively. Meanwhile, for drum separation on the DSD100 dataset, the model achieves a median signal-to-source distortion ratio of 4.36 dB, which is 2.91 dB better than that of the Spectral Hierarchical Network model. For the separation of the bass sound and the human voice, the model's in the separation of bass and human voice, the median distortion ratio of the model is as high as 4.87 dB and 6.09 dB, which is better than that of the Spectral Hierarchical Network model. This indicates the significant performance advantages in feature extraction and separation of music sources, and it has important application values in music production and speech recognition.http://www.sciencedirect.com/science/article/pii/S2772941924000784Improved attention mechanismPhase featureMusic sourceFeature extractionSAM |
| spellingShingle | Weina Yu Music source feature extraction based on improved attention mechanism and phase feature Systems and Soft Computing Improved attention mechanism Phase feature Music source Feature extraction SAM |
| title | Music source feature extraction based on improved attention mechanism and phase feature |
| title_full | Music source feature extraction based on improved attention mechanism and phase feature |
| title_fullStr | Music source feature extraction based on improved attention mechanism and phase feature |
| title_full_unstemmed | Music source feature extraction based on improved attention mechanism and phase feature |
| title_short | Music source feature extraction based on improved attention mechanism and phase feature |
| title_sort | music source feature extraction based on improved attention mechanism and phase feature |
| topic | Improved attention mechanism Phase feature Music source Feature extraction SAM |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000784 |
| work_keys_str_mv | AT weinayu musicsourcefeatureextractionbasedonimprovedattentionmechanismandphasefeature |