Meta-learning in Audio and Speech Processing: An End to End Comprehensive Review
Published in International Conference on Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2024), 2024
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.
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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.
Published in: International Conference on Multi-disciplinary Trends in Artificial Intelligence Pages: 140-154 Publisher: Springer Nature Singapore Date: November 12, 2024
Citation: Raimon, A., Masti, S., Sateesh, S. K., Vengatagiri, S., & Das, B. (2024). "Meta-learning in Audio and Speech Processing: An End to End Comprehensive Review." International Conference on Multi-disciplinary Trends in Artificial Intelligence. Springer Nature Singapore, 140-154.
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