2024/05/05 by June-Woo Kim, Miika Toikkanen, Kim, June-Woo +7 · 1 citation
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Chronic Obstructive Pulmonary Disease (COPD) Research #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Phonocardiography and Auscultation Techniques #Sound (cs.SD) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.02996
openalex publication_date 2024/05/05 · openalex created_date 2024/05/09 · openalex updated_date 2026/07/28
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%.