In a recent study, researchers tackled a 90% imbalance in a lung sound dataset used for machine learning. The team employed various augmentations to increase the number of audio files for underrepresented classes. The critical question they faced was whether these augmented files remained representative of the original lung sounds, such as those indicative of asthma. The study aimed to justify the use of these augmentations while ensuring the modified audio files still accurately represented the specific conditions they were meant to depict. The findings suggest that with careful application, augmentations can help balance datasets without compromising the integrity of the original audio files.
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