Abstract / Summary
Automatic characterization of adventitious respiratory sounds is essential for developing computer-aided systems that support respiratory disease diagnosis. Commonly used acoustic descriptors, such as Mel frequency cepstral coefficients (MFCCs), primarily characterize spectral information and may not fully capture the nonlinear temporal dynamics of respiratory signals. In this work, we investigate the discriminative capability of Generalized Weighted Ordinal Pattern (GWOP) descriptors for representing adventitious respiratory sounds. Using respiratory recordings from the publicly available SPRSound pediatric database, GWOP descriptors were computed over multiple embedding dimensions, weighting factors, and time delays. An XGBoost classifier was employed exclusively as a supervised validation model to assess the discriminative information contained in the proposed feature representation, while the XGBoost Gain criterion was used to analyze the contribution of individual descriptor components. The proposed representation achieved stable performance across cross-validation folds, outperformed a conventional MFCC representation under the same evaluation protocol, and demonstrated that a reduced subset of Gain selected features preserved, and slightly improved, the classification performance while substantially reducing the dimensionality of the original feature space. These findings indicate that GWOP provides a lighter yet more informative and interpretable representation of adventitious respiratory sounds, capturing nonlinear ordinal dynamics that complement conventional spectral descriptors and offering a promising alternative for respiratory sound characterization.