Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913734069321728 |
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| author | Raimon, Athul Masti, Shubha Sateesh, Shyam K Vengatagiri, Siyani Das, Bhaskarjyoti |
| author_facet | Raimon, Athul Masti, Shubha Sateesh, Shyam K Vengatagiri, Siyani Das, Bhaskarjyoti |
| contents | This survey overviews various meta-learning approaches used in audio and speech processing scenarios. Meta-learning is used where model performance needs to be maximized with minimum annotated samples, making it suitable for low-sample audio processing. Although the field has made some significant contributions, audio meta-learning still lacks the presence of comprehensive survey papers. We present a systematic review of meta-learning methodologies in audio processing. This includes audio-specific discussions on data augmentation, feature extraction, preprocessing techniques, meta-learners, task selection strategies and also presents important datasets in audio, together with crucial real-world use cases. Through this extensive review, we aim to provide valuable insights and identify future research directions in the intersection of meta-learning and audio processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review Raimon, Athul Masti, Shubha Sateesh, Shyam K Vengatagiri, Siyani Das, Bhaskarjyoti Sound Machine Learning Audio and Speech Processing This survey overviews various meta-learning approaches used in audio and speech processing scenarios. Meta-learning is used where model performance needs to be maximized with minimum annotated samples, making it suitable for low-sample audio processing. Although the field has made some significant contributions, audio meta-learning still lacks the presence of comprehensive survey papers. We present a systematic review of meta-learning methodologies in audio processing. This includes audio-specific discussions on data augmentation, feature extraction, preprocessing techniques, meta-learners, task selection strategies and also presents important datasets in audio, together with crucial real-world use cases. Through this extensive review, we aim to provide valuable insights and identify future research directions in the intersection of meta-learning and audio processing. |
| title | Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2408.10330 |