Papers Few-Shot Audio Classification
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SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification
Few-shot classification (FSC) is widely used for learning from limited labeled data, yet most evaluations implicitly assume that target concepts are independent of contextual cues. In real-world settings, however, exampl…
Few-Shot Image ClassificationFew-Shot Audio ClassificationPrototypical Contrastive Learning For Improved Few-Shot Audio Classification
Few-shot learning has emerged as a powerful paradigm for training models with limited labeled data, addressing challenges in scenarios where large-scale annotation is impractical. While extensive research has been conduc…
Few-Shot Audio ClassificationContrastive LearningFew-Shot LearningEpisodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification
The Prototypical Network (ProtoNet) has emerged as a popular choice in Few-shot Learning (FSL) scenarios due to its remarkable performance and straightforward implementation. Building upon such success, we first propose …
Audio ClassificationFew-Shot Audio ClassificationFew-Shot LearningOn the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification
In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data. Networks pretrained through self-supervision serve as effective feature extractors for…
Audio ClassificationFew-Shot Audio ClassificationFew-Shot LearningSelf-Supervised LearningAcoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities
The auditory system plays a substantial role in shaping the overall human perceptual experience. While prevailing large language models (LLMs) and visual language models (VLMs) have shown their promise in solving a wide …
Audio ClassificationFew-Shot Audio ClassificationLanguage ModelingLanguage Modelling+1MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations
Contrastive self-supervised learning has gained attention for its ability to create high-quality representations from large unlabelled data sets. A key reason that these powerful features enable data-efficient learning o…
Few-Shot Audio ClassificationInductive BiasSelf-Supervised LearningUnsupervised Few-Shot Audio ClassificationHalluAudio: Hallucinating Frequency as Concepts for Few-Shot Audio Classification
Few-shot audio classification is an emerging topic that attracts more and more attention from the research community. Most existing work ignores the specificity of the form of the audio spectrogram and focuses largely on…
Audio ClassificationFew-Shot Audio ClassificationSpecificityMetaAudio: A Few-Shot Audio Classification Benchmark
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. This work aims to alleviate this relia…
Audio ClassificationClassificationFew-Shot Audio ClassificationFew-Shot Learning+3Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
Few-shot learning aims to generalize unseen classes that appear during testing but are unavailable during training. Prototypical networks incorporate few-shot metric learning, by constructing a class prototype in the for…
Audio ClassificationEvent DetectionFew-Shot Audio ClassificationFew-Shot Learning+5A Study of Few-Shot Audio Classification
Advances in deep learning have resulted in state-of-the-art performance for many audio classification tasks but, unlike humans, these systems traditionally require large amounts of data to make accurate predictions. Not …
Audio ClassificationBIG-bench Machine LearningClassificationFew-Shot Audio Classification+3