Design of Music Emotion Analysis and Creation Aid System Based on Machine Learning

Abstract

In human-centric contexts, visual vision plays an increasingly important role in decision making, learning, communication, and situation awareness. Music is a language that conveys emotion to all creatures, including plants and animals. These emotional characteristics are included in the emotion found in music, along with other feelings conveyed via melodies, rhythms, and composer-highlighted moments. Human interaction, communication, decision-making, and cognitive processes are all heavily influenced by emotion. However, Subjective music emotion classification is made more difficult by pure machine classification due to its objectivity; additionally, emotion classification has not reached the same level of accuracy as style classification due to the complex and poorly understood mechanisms underlying how humans perceive music and generate emotions. This manuscript proposes a Multi-Scale Adaptive Graph Neural Network (MSAGNN)optimized with the Remora Optimization Algorithm (ROA) for predicting music emotion MEA-MSAGNN-ROA. Initially data is taken from Music Video Emotion (MVE) dataset Afterward the data is fed to Attribute-based Neural Collaborative Filtering (AbNCF) based pre-processing process. The outcome from the pre-processing data is transferred to the MSAGNN. The music emotions are successfully classified by using MSAGNN are exciting, fear, sad and relaxation. The ROA is used to optimize the weight parameter of MSAGNN. The proposed MEA-MSAGNN-ROA is applied in Python working platform. Performance parameters, like accuracy, precision, sensitivity, F1-score, ROC and recall analyzed to compute proposed approach. The proposed MEA-MSAGNN-ROA approach yields improved results in terms of accuracy (22.46%, 38.58%, 21.74%), sensitivity (21.97%, 33.88%, 25.52%), and precision. reduced computation times of 21.86%, 36.76%, 28.95%, and 81.46%, 95.97%, 86.77%.The proposed MEA-MSAGNN-ROA method is compared with the existing methods such as MEA-CNN, MEA-BPNN, and MEA-FNN, respectively. From the result it is concludes that the proposed MEA-MSAGNN-ROA method based accuracy is higher than the existing methods.