Explainability-Guided Multimodal Optimization for Arabic Music Genre Classification

Classifying Arabic music genres is a challenging task because Arabic music encompasses a wide range of rhythms, melodies, and musical structures. Multimodal learning combines complementary information from multiple representations to improve classification; however, it can produce new feature spaces with potentially weak or redundant dimensions that can adversely affect classification performance. Explainable AI methods such as SHapley Additive exPlanations (SHAP) are usually used after model training for interpretation, yet they are not often used to improve the training process itself. In this work, we propose an explainability-guided evolutionary optimization method for audio–symbolic Arabic music genre classification. First, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Evolution Strategy (ES) are applied directly to the original fused representations to establish optimization-only baselines. Next, the framework uses global SHAP importance values and transforms them into bounded continuous feature weights, assigning higher weights to the most informative dimensions. A validation-guided pruning step is then performed to remove low-importance dimensions only when their removal improves validation performance. The resulting representation is used for evolutionary classifier hyperparameter optimization. Experiments are conducted on 1800 aligned audio–symbolic pairs from six Arabic music genres. Under the matched per-seed optimization protocol, the validation-selected SHAP-refined CNN14+BERT system achieved 95.30% mean accuracy, compared with 94.40% for the corresponding optimization-only control. The additional accuracy differences between the SHAP-refined and matched optimization-only conditions were +0.90, +0.50, +0.20, and 0.00 percentage points for CNN14+BERT, CNN14+RoBERTa, ResNet50+RoBERTa, and MobileNetV3-Large+XLNet, respectively. Across the staged controls, evolutionary optimization was the largest incremental contributor to the final performance gain, while SHAP-guided representation refinement provided a smaller, architecture-dependent benefit beyond the matched optimization-only controls.