AudioFuse is a hybrid architecture that jointly learns from spectrograms and raw waveforms to classify phonocardiograms. It combines a wide-and-shallow Vision Transformer for spectral features with a shallow 1D CNN for temporal waveforms, reducing overfitting while capturing complementary information. On the PhysioNet 2016 dataset, AudioFuse achieves a state‑of‑the‑art ROC‑AUC of 0.8608 and shows superior robustness to domain shift on the PASCAL dataset, outperforming both spectrogram‑only and waveform‑only baselines.
By Md. Saiful Bari Siddiqui, Utsab Saha
arXiv:2610.27942v1 Announce Type: cross
Abstract: Automated respiratory sound classification enables continuous, non-invasive respiratory assessment, yet long-term deployment remains challenging due...
By Xinhong Li, Weiping Tu
arXiv:2508. 02349v2 Announce Type: replace-cross Abstract: Monitoring respiration parameters such as respiratory rate could be beneficial to understand the impact of training on equine health and performance and ultimately improve equine welfare.
By Jeanne I. M. Parmentier (Utrecht University, University of Twente, Inertia Technology B.V), Rhana M. Aarts (Utrecht University), Elin Hernlund (Swedish University of Agricultural Sciences), Marie Rhodin (Swedish University of Agricultural Sciences), Berend Jan van der Zwaag (University of Twente, Inertia Technology B.V)
arXiv:2408.16030v3 Announce Type: replace-cross
Abstract: Objective This study evaluated deep learning models, specifically ResNet-50 and Audio Spectrogram Transformer (AST), to identify multi-level...
By Ying-Chieh Hsu, Stanley Yung-Chuan Liu, Chao-Jung Huang, Chi-Wei Wu, Ren-Kai Cheng, Jane Yung-Jen Hsu, Shang-Ran Huang, Yuan-Ren Cheng, Fu-Shun Hsu
arXiv:2606. 08247v1 Announce Type: cross Abstract: Acute asthma risk assessment requires rapid interpretation of respiratory sounds, oxygenation, airflow limitation, speech ability, work of breathing, mental status, and response to reliever therapy.
By Aueaphum Aueawatthanaphisut
arXiv:2606. 11922v1 Announce Type: cross Abstract: Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Transformer (AST).
By Hemansh Shridhar, Miika Toikkanen, June-Woo Kim
The study compares classical and deep learning classifiers for a five‑class respiratory‑sound dataset of 1,211 clips. An SVM using 30 summary features (MFCC, ZCR, RMS) achieves macro recall of 0.78, while a 1.13M‑parameter convolutional‑recurrent network reaches 0.75–0.77; no clear advantage is found between model families. The largest performance gain comes from standardizing the SVM’s features, which boosts macro recall by 27 points, far exceeding any architectural difference.
By Abubakar Chilala, Cleophas Kadima, Innocent Anyaele, Victor Olufemi
WIPSNet is a 3D ResNet that processes stacked continuous wavelet transform scalograms of overnight impedance pneumography (IP) signals to detect paediatric wheeze. In a study of 15 patients (60 nights, 281 hours), it achieved an AUC of 0.783 ± 0.026, outperforming the traditional Expiratory Variability Index, a state‑space model, and two modern sleep‑staging architectures. The model’s best performance occurs with a 32‑minute temporal context, highlighting the importance of multi‑scale temporal aggregation for nocturnal respiratory dynamics.
By Felix Oury, Harley Day, Karina Mayoral, Ville-Pekka Sepp\"a, Sejal Saglani, Reiko J. Tanaka
BreathGRU is a semi‑supervised Bidirectional Gated Recurrent Unit framework designed to segment speech and breath events in respiratory audio. It combines acoustic feature extraction, bidirectional recurrent modeling, pseudo‑label refinement, and duration‑constrained Segmental Viterbi decoding to produce accurate speech‑breath segmentation. In evaluations against existing methods, BreathGRU achieved the highest breath event recall, lowest onset‑localisation error, and highest Mean Match Intersection over Union, outperforming large pretrained VAD models such as Silero.
By Sania Fatima Sayed, John W. Holloway, Reyer Zwiggelaar, Faisal I. Rezwan
arXiv:2606. 10972v1 Announce Type: cross Abstract: This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrograms using deep learning.
By Ipek Sen, Ozgur Ozdemir, Elena Battini Sonmez
arXiv:2606. 17339v1 Announce Type: new Abstract: Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems.
By Sejal Bhalla, Larry Kieu, Aina Merchant, Eyal de Lara, Alex Mariakakis
The paper introduces BTS-CAFE, a federated domain generalization framework for respiratory sound classification that addresses stethoscope-induced shortcuts. It combines causality-inspired device-style interventions, counterfactual metadata augmentation, and gradient alignment to reduce style–content entanglement and promote device-invariant decision boundaries. Experiments on ICBHI and SPRSound datasets show a 3.69‑point improvement in out-of-distribution performance over the baseline and outperform conventional data augmentation and federated learning methods.
By Heejoon Koo, Yoon Tae Kim, Miika Toikkanen, June-Woo Kim