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Few-Shot Audio Classification

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Few-shot classification for audio signals. Presents a unique challenge compared to other few-shot domains as we deal with temporal dependencies as well.

Like other few-shot problems, few-shot audio classification can be tackled in a variety of ways, from using supervised meta-learning on the same primary dataset, to pre-training on an external dataset and utilising linear readout. For this reason, results in each dataset leaderboard should be correctly tagged e.g. with "Within Dataset Meta-Learning" etc

논문

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Few-Shot Audio Classification

논문

Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification

On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification

Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities

MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations

HalluAudio: Hallucinating Frequency as Concepts for Few-Shot Audio Classification

MetaAudio: A Few-Shot Audio Classification Benchmark

Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining

A Study of Few-Shot Audio Classification