Toward an Acoustic Characterization of Street Cries: A Machine Learning-Based Approach with Parsimonious Feature Selection

Street cries are vocal expressions used by hawkers to advertise products or services in public spaces. Although they may reflect adaptation to noisy urban environments, their acoustic characteristics remain poorly understood. This study evaluated whether street cries (SC) differ acoustically from the hawkers’ normal speaking voices (NV) and whether these differences can be captured using compact feature subsets. Sixteen acoustic features related to fundamental frequency, formant structure, spectral properties, and voice quality were extracted. Redundant features were removed based on Kendall’s τ correlations, after which four classifiers were evaluated: support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), and Gaussian naive Bayes (GNB). Binary particle swarm optimization (BPSO) was then used for feature selection, with α controlling the trade-off between cross-validated balanced accuracy and subset size. In repeated participant-grouped cross-validation, SVM achieved the highest mean balanced accuracy with the 13-feature vector (0.887 ± 0.056). On the held-out test set, BPSO-selected subsets yielded balanced accuracies of 0.783–0.900. The best SVM and GNB configurations both achieved 0.900 using six and four features, respectively. The features f0 and F4 appeared in all selected subsets. These results indicate that SC and NV can be differentiated using parsimonious, interpretable acoustic feature sets.