SPIRA-BM: Biomarkers for Respiratory Conditions by Audio Analysis via Artificial Intelligence

• Marcelo Finger USP
• Larissa C. Berti UNESP
• Elisa Y. Nakagawa USP
• Celso R. F. de Carvalho USP
• Beatriz R. de Medeiros USP
• Flaviane R. F. Svartman USP
• Marcelo G. Queiroz USP
• Arnaldo Cândido Jr UNESP
• Marcelo M. Gauy UNESP
• Murilo G. Gazzola UPM
• Marcus V. M. Martins USP
• Jaqueline Scholz Hospital das Clínicas FMUSP
• Sara Ziotti Hospital das Clínicas FMUSP

DOI: https://doi.org/10.5753/sbcas.2025.7147

Abstract

This paper presents an ongoing project that aims to investigate audio biomarkers for respiratory conditions such as Respiratory Insufficiency, severe asthma, and smoking. The SPIRA-BM project is currently developing inexpensive detectors for these biomarkers, using audio collection and processing on mobile computing devices, and using machine learning, artificial intelligence, and signal analysis techniques.

References

Aluisio, S. et al. (2022). Detecting respiratory insufficiency via voice analysis: The Spira Project. In Practical Machine Learning for Developing Countries. ICLR.

Atmaja, B. T., Asmoro, W. A., Sasou, A., et al. (2025). Cross-dataset COVID-19 transfer learning with data augmentation. Int. J. of Information Technology, pages 1–14.

Biomarkers Definitions Working Group (2001). Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clinical Pharmacology & Therapeutics, 69(3):89–95.

Botelho, M. C., Trancoso, I., Abad, A., and Paiva, T. (2019). Speech as a biomarker for obstructive sleep apnea detection. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5851–5855. IEEE.

Brown, C. et al. (2020). Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’20, page 3474–3484. ACM.

Casanova, E., Candido Jr., A., Fernandes Jr., R. C., Finger, M., Gris, L. R. S., Ponti, M. A., and Pinto da Silva, D. P. (2021a). Transfer Learning and Data Augmentation Techniques to the COVID-19 Identification Tasks in ComParE 2021. In Proc. Interspeech 2021, pages 446–450. Stefan Steidl Computational Paralinguistics Award, COVID-19 Cough Sub-Challenge Prize.

Casanova, E. et al. (2021b). Deep learning against COVID-19: Respiratory insufficiency detection in brazilian portuguese speech. In Findings of the ACL. ACL.

Coppock, H. et al. (2022). Audio-based ai classifiers show no evidence of improved covid-19 screening over simple symptoms checkers. arXiv:2212.08570.

Gauy, M. and Finger, M. (2021a). Audio mfcc-gram transformers for respiratory insufficiency detection in covid-19. In STIL2021, pages 143–152, Porto Alegre, RS, Brazil.

Gauy, M. M. et al. (2023). Discriminant audio properties in deep learning based respiratory insufficiency detection in brazilian portuguese. In Artificial Intelligence in Medicine, AIME 2023, page 271–275, Berlin, Heidelberg. Springer-Verlag.

Gauy, M. M. and Finger, M. (2021b). Audio mfcc-gram transformers for respiratory insufficiency detection in covid-19. In STIL 2021 ().

Gauy, M. M. and Finger, M. (2023). Acoustic models of brazilian portuguese speech based on neural transformers. arXiv preprint arXiv:2312.09265.

Han, J., Xia, T., Spathis, D., Bondareva, E., Brown, C., Chauhan, J., Dang, T., Grammenos, A., Hasthanasombat, A., Floto, A., et al. (2022). Sounds of covid-19: exploring realistic performance of audio-based digital testing. NPJ digital medicine, 5(1):1–9.

Huang, P.-Y., Xu, H., Li, J., Baevski, A., Auli, M., Galuba, W., Metze, F., and Feichtenhofer, C. (2022). Masked autoencoders that listen. Advances in Neural Information Processing Systems, 35:28708–28720.

Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., and Nabeel, M. (2020). Ai4covid-19: Ai enabled preliminary diagnosis for covid-19 from cough samples via an app. Informatics in Medicine Unlocked, 20:100378.

Kong, Q., Cao, Y., Iqbal, T., Wang, Y., Wang, W., and Plumbley, M. D. (2020). Panns: Large-scale pretrained audio neural networks for audio pattern recognition.

Nevler, N., Ash, S., Irwin, D. J., Liberman, M., and Grossman, M. (2019). Validated automatic speech biomarkers in primary progressive aphasia. Annals of Clinical and Translational Neurology, 6(1):4–14.

Roberts, M. et al. (2021). Common pitfalls and recommendations for using machine learning to detect and prognosticate for covid-19 using chest radiographs and ct scans. Nature Machine Intelligence, 3:199–217.

Trancoso, I., Correia, M. J. R. F., Teixeira, F., Abad, A., Botelho, M. C. T., and Raj, B. (2019). Speech as a (private?) biomarker for speech affecting diseases. In In ICIEA 2019 - The 14th IEEE Conference on Industrial Electronics and Applications, Xi’an, China. IEEE. Keynote paper.

Wynants, L. et al. (2020). Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ, 369.