Open Set Recognition For Music Genre Classification
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866914794751131648 |
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| author | Liu, Kevin DeMori, Julien Abayomi, Kobi |
| author_facet | Liu, Kevin DeMori, Julien Abayomi, Kobi |
| contents | We explore segmentation of known and unknown genre classes using the open source GTZAN and FMA datasets. For each, we begin with best-case closed set genre classification, then we apply open set recognition methods. We offer an algorithm for the music genre classification task using OSR. We demonstrate the ability to retrieve known genres and as well identification of aural patterns for novel genres (not appearing in a training set). We conduct four experiments, each containing a different set of known and unknown classes, using the GTZAN and the FMA datasets to establish a baseline capacity for novel genre detection. We employ grid search on both OpenMax and softmax to determine the optimal total classification accuracy for each experimental setup, and illustrate interaction between genre labelling and open set recognition accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_07548 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Open Set Recognition For Music Genre Classification Liu, Kevin DeMori, Julien Abayomi, Kobi Audio and Speech Processing Optimization and Control 90C90, 6208 E.4; G.4 We explore segmentation of known and unknown genre classes using the open source GTZAN and FMA datasets. For each, we begin with best-case closed set genre classification, then we apply open set recognition methods. We offer an algorithm for the music genre classification task using OSR. We demonstrate the ability to retrieve known genres and as well identification of aural patterns for novel genres (not appearing in a training set). We conduct four experiments, each containing a different set of known and unknown classes, using the GTZAN and the FMA datasets to establish a baseline capacity for novel genre detection. We employ grid search on both OpenMax and softmax to determine the optimal total classification accuracy for each experimental setup, and illustrate interaction between genre labelling and open set recognition accuracy. |
| title | Open Set Recognition For Music Genre Classification |
| topic | Audio and Speech Processing Optimization and Control 90C90, 6208 E.4; G.4 |
| url | https://arxiv.org/abs/2209.07548 |