AudioCIL: A Python Toolbox for Audio Class-Incremental Learning with Multiple Scenes
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916530491490304 |
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| author | Xu, Qisheng Sun, Yulin Su, Yi Zhu, Qian Tan, Xiaoyi Wen, Hongyu Gao, Zijian Xu, Kele Dou, Yong Feng, Dawei |
| author_facet | Xu, Qisheng Sun, Yulin Su, Yi Zhu, Qian Tan, Xiaoyi Wen, Hongyu Gao, Zijian Xu, Kele Dou, Yong Feng, Dawei |
| contents | Deep learning, with its robust aotomatic feature extraction capabilities, has demonstrated significant success in audio signal processing. Typically, these methods rely on static, pre-collected large-scale datasets for training, performing well on a fixed number of classes. However, the real world is characterized by constant change, with new audio classes emerging from streaming or temporary availability due to privacy. This dynamic nature of audio environments necessitates models that can incrementally learn new knowledge for new classes without discarding existing information. Introducing incremental learning to the field of audio signal processing, i.e., Audio Class-Incremental Learning (AuCIL), is a meaningful endeavor. We propose such a toolbox named AudioCIL to align audio signal processing algorithms with real-world scenarios and strengthen research in audio class-incremental learning. Code is available at https://github.com/colaudiolab/AudioCIL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11907 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AudioCIL: A Python Toolbox for Audio Class-Incremental Learning with Multiple Scenes Xu, Qisheng Sun, Yulin Su, Yi Zhu, Qian Tan, Xiaoyi Wen, Hongyu Gao, Zijian Xu, Kele Dou, Yong Feng, Dawei Sound Audio and Speech Processing Deep learning, with its robust aotomatic feature extraction capabilities, has demonstrated significant success in audio signal processing. Typically, these methods rely on static, pre-collected large-scale datasets for training, performing well on a fixed number of classes. However, the real world is characterized by constant change, with new audio classes emerging from streaming or temporary availability due to privacy. This dynamic nature of audio environments necessitates models that can incrementally learn new knowledge for new classes without discarding existing information. Introducing incremental learning to the field of audio signal processing, i.e., Audio Class-Incremental Learning (AuCIL), is a meaningful endeavor. We propose such a toolbox named AudioCIL to align audio signal processing algorithms with real-world scenarios and strengthen research in audio class-incremental learning. Code is available at https://github.com/colaudiolab/AudioCIL. |
| title | AudioCIL: A Python Toolbox for Audio Class-Incremental Learning with Multiple Scenes |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.11907 |