A Reproducible Evaluation of Hybrid Spectral–Temporal Features for Four-Class Respiratory Sound Event Classification
TL;DR
These findings apply to direct early concatenation with the shared 1D-CNN backbone and do not imply that feature fusion is generally ineffective, and do not imply that feature fusion is generally ineffective.
Abstract
Respiratory-sound event classification is challenged by non-stationarity, class imbalance, heterogeneous acquisition, and participant-correlated recordings. This study evaluates whether direct fusion of short-time Fourier transform (STFT), mel-frequency cepstral coefficient (MFCC), and wavelet-packet features improves a temporal one-dimensional convolutional neural network (1D-CNN), and whether temporal convolution offers an advantage over conventional classifiers. A radial basis function support vector machine (RBF-SVM) and random forest were included deliberately to separate the value of the engineered representation from classifier complexity. Experiments used 920 recordings and 6898 annotated cycles from the International Conference on Biomedical and Health Informatics (ICBHI) 2017 Respiratory Sound Database. The predefined 60:40 recording-level benchmark partition was retained; model selection used five-fold participant-grouped cross-validation, and 95% confidence intervals were estimated from 1000 participant-level bootstrap resamples. The complete hybrid 1D-CNN achieved a macro-averaged F1-score of 0.313. MFCC alone yielded 0.320, but the paired difference was not statistically resolved. The RBF-SVM and random forest achieved 0.401 and 0.378, respectively. These findings apply to direct early concatenation with the shared 1D-CNN backbone and do not imply that feature fusion is generally ineffective. The study provides leakage-aware baselines, controlled ablations, clustered uncertainty estimates, and frozen artifacts for reproducible comparison.