Automatic Album Sequencing
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916495847587840 |
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| author | Herrmann, Vincent Ashley, Dylan R. Schmidhuber, Jürgen |
| author_facet | Herrmann, Vincent Ashley, Dylan R. Schmidhuber, Jürgen |
| contents | Album sequencing is a critical part of the album production process. Recently, a data-driven approach was proposed that sequences general collections of independent media by extracting the narrative essence of the items in the collections. While this approach implies an album sequencing technique, it is not widely accessible to a less technical audience, requiring advanced knowledge of machine learning techniques to use. To address this, we introduce a new user-friendly web-based tool that allows a less technical audience to upload music tracks, execute this technique in one click, and subsequently presents the result in a clean visualization to the user. To both increase the number of templates available to the user and address shortcomings of previous work, we also introduce a new direct transformer-based album sequencing method. We find that our more direct method outperforms a random baseline but does not reach the same performance as the narrative essence approach. Both methods are included in our web-based user interface, and this -- alongside a full copy of our implementation -- is publicly available at https://github.com/dylanashley/automatic-album-sequencing |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07772 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Album Sequencing Herrmann, Vincent Ashley, Dylan R. Schmidhuber, Jürgen Machine Learning Artificial Intelligence Computation and Language Multimedia Sound Audio and Speech Processing 68T07 H.5.5; I.2.6; I.5.1; J.5 Album sequencing is a critical part of the album production process. Recently, a data-driven approach was proposed that sequences general collections of independent media by extracting the narrative essence of the items in the collections. While this approach implies an album sequencing technique, it is not widely accessible to a less technical audience, requiring advanced knowledge of machine learning techniques to use. To address this, we introduce a new user-friendly web-based tool that allows a less technical audience to upload music tracks, execute this technique in one click, and subsequently presents the result in a clean visualization to the user. To both increase the number of templates available to the user and address shortcomings of previous work, we also introduce a new direct transformer-based album sequencing method. We find that our more direct method outperforms a random baseline but does not reach the same performance as the narrative essence approach. Both methods are included in our web-based user interface, and this -- alongside a full copy of our implementation -- is publicly available at https://github.com/dylanashley/automatic-album-sequencing |
| title | Automatic Album Sequencing |
| topic | Machine Learning Artificial Intelligence Computation and Language Multimedia Sound Audio and Speech Processing 68T07 H.5.5; I.2.6; I.5.1; J.5 |
| url | https://arxiv.org/abs/2411.07772 |