C2GA uses conditional VQ-VAE with decoupled local tokens and global class prototypes plus a Transformer prior to generate high-fidelity label-consistent Mel-spectrograms for respiratory sound data augmentation.
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C2GA: A Class-Controllable Generative Augmentation Framework for Respiratory Sound Classification
C2GA uses conditional VQ-VAE with decoupled local tokens and global class prototypes plus a Transformer prior to generate high-fidelity label-consistent Mel-spectrograms for respiratory sound data augmentation.