Complete 5-fold stratified cross-validation procedure for hyperparameter search. (1) The dataset is randomly split into a training set and a testing set while maintaining equal proportions of each trumpet in both sets. (2) The training set is randomly split into 5 subsets (or folds) ensuring each trumpet’s proportion is preserved across folds. (3) The model is trained on 4 folds and performances are evaluated (validated) on the remaining fold. This process repeats 5 times, resulting in 5 trained models with associated validation performances. (4) The accuracy is computed and averaged over the 5 validation set. (5) Steps (3) and (4) are repeated with different hyperparameter sets, iterating this process 100 times. (6) The hyperparameter set yielding the highest average accuracy is selected. The final model is trained on the entire training set (from step 1) using these hyperparameters. The performance of the final model is evaluated on the testing set to assess its generalization ability.