Improved differential evolution algorithm based convolutional neural network for emotional analysis of music data

Abstract

Evolutionary computation is derived from the simulation of natural selection and genetic processes in biological evolution. This approach provides a method for optimizing the structure and parameters of neural networks. When combined with neural networks, forming what's termed as evolutionary computation based neural networks, it offers a systematic approach to optimize neural network models in diverse applications. In this study, we introduce a method that employs differential evolution algorithms to optimize parameters of convolutional neural network (CNN) for music emotion recognition tasks. This method optimizes the initial weights of the CNN, aiming to achieve near-global optimal solutions and expedite network convergence. Comparative experiments indicate that the proposed approach effectively identifies optimal parameters and structures for CNN, suggesting potential advancements in automated music emotion recognition.