Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review
<|reference_start|>Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review: 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.<|reference_end|>
@article{raimon2024meta-learning,
    title={Meta-Learning in Audio and Speech Processing: An End to End
  Comprehensive Review},
    author={Athul Raimon, Shubha Masti, Shyam K Sateesh, Siyani Vengatagiri,
  Bhaskarjyoti Das},
    journal={arXiv preprint arXiv:2408.10330},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10330},
    primaryClass={cs.SD cs.LG eess.AS}
}