Advanced music classification using a combination of capsule neural network by upgraded ideal gas molecular movement algorithm

Music genres classification has long been a challenging task in the field of Music Information Retrieval (MIR) due to the intricate and diverse nature of musical content. Traditional methods have struggled to accurately capture the complex patterns that differentiate one genre from another. However, recent advancements in deep learning have presented new opportunities to tackle this challenge. One such approach is the use of Capsule Neural Networks (CapsNet), which have shown promise in capturing hierarchical relationships within data. Nevertheless, the performance of CapsNet models heavily depends on the optimal configuration of their parameters, which is a complex task. To address this issue, this research proposes a novel methodology that combines CapsNet with an upgraded version of the Ideal Gas Molecular Movement (UIGMM) optimization algorithm. By utilizing the UIGMM algorithm, the parameters of the CapsNet model can be fine-tuned, thereby enhancing its ability to accurately recognize and classify different music genres. The effectiveness of this proposed model is evaluated using three benchmark datasets: ISMIR2004, GTZAN, and Extended Ballroom. Through comparative analysis against state-of-the-art models, the proposed approach demonstrates superior performance, highlighting its potential as a robust tool for music genre classification.