mlsanityData to 7 Oct 2026

Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review

arXiv 2408.10330 ↗First posted 19 Aug 2024cs.SD cs.LG eess.AScited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 19 Aug 2024.

Builds on

23 of 43 references.

References · 23

  1. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  2. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  3. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  4. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  5. 2017VoxCeleb: a large-scale speaker identification datasetcited by 714
  6. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  7. 2020FSD50K: An Open Dataset of Human-Labeled Sound Eventscited by 339
  8. 2019Meta Learning for End-to-End Low-Resource Speech Recognitioncited by 29
  9. 2022MetaAudio: A Few-Shot Audio Classification Benchmarkcited by 24
  10. 2021Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speechcited by 22
  11. 2018Learning to match transient sound events using attentional similarity for few-shot sound recognitioncited by 13
  12. 2020Few-shot acoustic event detection via meta-learningcited by 13
11 more
  1. 2021Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognitioncited by 13
  2. 2021Who calls the shots? Rethinking Few-Shot Learning for Audiocited by 11
  3. 2018Training neural audio classifiers with few datacited by 8
  4. 2019Few Shot Speaker Recognition using Deep Neural Networkscited by 8
  5. 2022Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognitioncited by 8
  6. 2022Improved Meta Learning for Low Resource Speech Recognitioncited by 7
  7. 2023Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypescited by 6
  8. 2020A Study of Few-Shot Audio Classificationcited by 5
  9. 2022Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detectioncited by 5
  10. 2019Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Eventscited by 4
  11. 2021Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Miningcited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognitioncited by 4
  2. 2025A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domainscited by 0