RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification

• URL: http://arxiv.org/abs/2405.02996v1
• Date: Sun, 5 May 2024 16:45:46 GMT
• Title: RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification
• Authors: June-Woo Kim, Miika Toikkanen, Sangmin Bae, Minseok Kim, Ho-Young Jung,
• Abstract summary: This paper explores the efficacy of pretrained speech models for respiratory sound classification. We find that there is a characterization gap between speech and lung sound samples, and to bridge this gap, data augmentation is essential. We propose RepAugment, an input-agnostic representation-level augmentation technique that outperforms SpecAugment.
• Score: 2.812716452984433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in AI have democratized its deployment as a healthcare assistant. While pretrained models from large-scale visual and audio datasets have demonstrably generalized to this task, surprisingly, no studies have explored pretrained speech models, which, as human-originated sounds, intuitively would share closer resemblance to lung sounds. This paper explores the efficacy of pretrained speech models for respiratory sound classification. We find that there is a characterization gap between speech and lung sound samples, and to bridge this gap, data augmentation is essential. However, the most widely used augmentation technique for audio and speech, SpecAugment, requires 2-dimensional spectrogram format and cannot be applied to models pretrained on speech waveforms. 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. Experimental results show that our approach outperforms the SpecAugment, demonstrating a substantial improvement in the accuracy of minority disease classes, reaching up to 7.14%.

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