Visual Design of Emotional Expressions of Music Art on Mobile Devices

Music is a powerful way to express emotions, and as information visualization develops, visualizing emotions in music has become a popular topic. This study proposes a strategy for visualizing emotions in music on mobile devices. It uses the activation-degree-effectiveness emotion model and combines the residual phase with mel-frequency cepstral coefficient weighting to extract emotion features. Convolutional and recurrent neural networks were optimized and used together to recognize musical emotions. Experimental results show that the proposed method achieves the highest recognition accuracy of 90% and 92% in the Sound-track dataset and Song’s dataset, respectively, and an error rate of 10% in the AMG1608 dataset. The accuracy for recognizing happiness, sadness, relaxation, and anger is above 88%. This study provides a feasible direction for optimizing the visual design of expression of emotion in music art and recognition of emotion in music.