Complex audio signal data compression and reconstruction: A benchmark data pre-processing approach for machine classification of chronic respiratory diseases

Abstract

Objective: To develop and evaluate innovative methods for compressing and reconstructing complex audio signals from medical auscultation, while maintaining diagnostic integrity and reducing dimensionality for machine classification. Methods: Using the ICBHI Respiratory Challenge 2017 Database, we assessed various compression frameworks, including discrete Fourier transform with peak detection, time-frequency transforms, dictionary learning and singular value decomposition. Reconstruction quality was evaluated using mean squared error (MSE). The study has been conducted at Bournemouth University from January 2023 to 2024. Results: The multi-resolution wavelet transform (MRWT) framework demonstrated superior performance with the lowest average MSE score of 0.037. The proposed time-frequency framework with MRWT achieved 80% accuracy in distinguishing chronic obstructive pulmonary disease from healthy samples. Conclusion: Our study advances signal processing in medical auscultation, while it offers insights into effective compression and reconstruction methods for preserving diagnostic information. The MRWT approach shows promising outcomes for balancing compression efficiency and reconstruction accuracy in complex audio signals.