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Found 2 papers, 1 papers with code
RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification
To address this, we propose RepAugment, an input-agnostic representation-level augmentation technique that outperforms SpecAugment, but is also suitable for respiratory sound classification with waveform pretrained models.
Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance
In this work, we propose a straightforward approach to augment imbalanced respiratory sound data using an audio diffusion model as a conditional neural vocoder.
Ranked #2 on
Audio Classification
on ICBHI Respiratory Sound Database
(using extra training data)
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