Deep learning model using squeezenet and promoted ideal gas molecular motion for music genre classification from audio spectrograms.

It may be argued that music genre classification (MGC) is one of the most important tasks in music information retrieval; however, it still suffers from being a high-dimensional, highly variable, and noisy audio signal. Most traditional deep learning models require large computational setups and do not fare well in the instances of overfitting and local optima. The paper proposes a new hybridization: SqueezeNet optimized through PIGMM (Promoted Ideal Gas Molecular Motion) for enhanced MGC performance. PIGMM, which is a metaheuristic algorithm with roots in molecular dynamics and is improved by chaos theory and opposition-based learning, was used to optimize the parameters of SqueezeNet for improved convergence and generalization. The model that works on audio spectrograms demonstrates 96% accuracy in feature extraction. Under ten-fold cross-validation on the GTZAN and Extended Ballroom datasets, the method achieves classification accuracies of 91.1% and 93.4%, respectively, both of which outperform state-of-the-art models. The results show the highest precision values of 93.5% and 95.8% as well as recall values of 96.5% and 97.7%, thus confirming the strength and effectiveness of this model. The work presents a lightweight and noise-resilient solution for scalable music classification.