Transfer Learning with Pseudo Multi-Label Birdcall Classification for DS@GT BirdCLEF 2024
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917715428507648 |
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| author | Miyaguchi, Anthony Cheung, Adrian Gustineli, Murilo Kim, Ashley |
| author_facet | Miyaguchi, Anthony Cheung, Adrian Gustineli, Murilo Kim, Ashley |
| contents | We present working notes for the DS@GT team on transfer learning with pseudo multi-label birdcall classification for the BirdCLEF 2024 competition, focused on identifying Indian bird species in recorded soundscapes. Our approach utilizes production-grade models such as the Google Bird Vocalization Classifier, BirdNET, and EnCodec to address representation and labeling challenges in the competition. We explore the distributional shift between this year's edition of unlabeled soundscapes representative of the hidden test set and propose a pseudo multi-label classification strategy to leverage the unlabeled data. Our highest post-competition public leaderboard score is 0.63 using BirdNET embeddings with Bird Vocalization pseudo-labels. Our code is available at https://github.com/dsgt-kaggle-clef/birdclef-2024 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06291 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transfer Learning with Pseudo Multi-Label Birdcall Classification for DS@GT BirdCLEF 2024 Miyaguchi, Anthony Cheung, Adrian Gustineli, Murilo Kim, Ashley Sound Audio and Speech Processing We present working notes for the DS@GT team on transfer learning with pseudo multi-label birdcall classification for the BirdCLEF 2024 competition, focused on identifying Indian bird species in recorded soundscapes. Our approach utilizes production-grade models such as the Google Bird Vocalization Classifier, BirdNET, and EnCodec to address representation and labeling challenges in the competition. We explore the distributional shift between this year's edition of unlabeled soundscapes representative of the hidden test set and propose a pseudo multi-label classification strategy to leverage the unlabeled data. Our highest post-competition public leaderboard score is 0.63 using BirdNET embeddings with Bird Vocalization pseudo-labels. Our code is available at https://github.com/dsgt-kaggle-clef/birdclef-2024 |
| title | Transfer Learning with Pseudo Multi-Label Birdcall Classification for DS@GT BirdCLEF 2024 |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.06291 |