Few-Shot Audio Classification
5 papers with code • 10 benchmarks • 9 datasets
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
Benchmarks
These leaderboards are used to track progress in Few-Shot Audio Classification
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Datasets
VoxCeleb1
Common Voice
ESC-50
NSynth
CREMA-D
FSDKaggle2018
Watkins Marine Mammal Sounds
Speech Accent Archive
Most implemented papers
Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
Moreover, we improve the framework of audio language model by using interleaved audio-text embeddings as the input sequence.
MetaAudio: A Few-Shot Audio Classification Benchmark
cheggan/metaaudio-a-few-shot-audio-classification-benchmark
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Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification.
MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations
cheggan/mt-slvr
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Contrastive self-supervised learning has gained attention for its ability to create high-quality representations from large unlabelled data sets.
On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification
CHeggan/Few-Shot-Classification-for-Audio-Evaluation
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In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data.
Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification
zdsy/proto-MAML
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The Prototypical Network (ProtoNet) has emerged as a popular choice in Few-shot Learning (FSL) scenarios due to its remarkable performance and straightforward implementation.
