Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914948808966144 |
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| author | Grötschla, Florian Strässle, Luca Lanzendörfer, Luca A. Wattenhofer, Roger |
| author_facet | Grötschla, Florian Strässle, Luca Lanzendörfer, Luca A. Wattenhofer, Roger |
| contents | Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. Although these relationships provide valuable insights for predictions, new music pieces or artists often face the cold-start problem due to insufficient initial information. To address this, one can extract content-based information directly from the music to enhance collaborative-filtering-based methods. While previous approaches have relied on hand-crafted audio features for this purpose, we explore the use of contrastively pretrained neural audio embedding models, which offer a richer and more nuanced representation of music. Our experiments demonstrate that neural embeddings, particularly those generated with the Contrastive Language-Audio Pretraining (CLAP) model, present a promising approach to enhancing music recommendation tasks within graph-based frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks Grötschla, Florian Strässle, Luca Lanzendörfer, Luca A. Wattenhofer, Roger Sound Artificial Intelligence Audio and Speech Processing Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. Although these relationships provide valuable insights for predictions, new music pieces or artists often face the cold-start problem due to insufficient initial information. To address this, one can extract content-based information directly from the music to enhance collaborative-filtering-based methods. While previous approaches have relied on hand-crafted audio features for this purpose, we explore the use of contrastively pretrained neural audio embedding models, which offer a richer and more nuanced representation of music. Our experiments demonstrate that neural embeddings, particularly those generated with the Contrastive Language-Audio Pretraining (CLAP) model, present a promising approach to enhancing music recommendation tasks within graph-based frameworks. |
| title | Towards Leveraging Contrastively Pretrained Neural Audio Embeddings for Recommender Tasks |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.09026 |