Music Emotion Recognition and Analysis Based on Neural Network

Abstract: The close connection between music and human emotions has always been an important topic of research in psychology and musicology. Scientists have proven that music can affect a person's emotional state, thereby possessing the potential for therapy and stress relief. With the development of information technology, automatic music emotion recognition has become an important research direction. The MultiSpec-DNN model proposed in this article is a multi-spectral deep neural network that integrates multiple features and modalities of music, including but not limited to melody, rhythm, harmony, and lyrical content, thus achieving efficient and accurate recognition of music emotions. The core of the MultiSpec-DNN model lies in its ability to process and analyze various types of data inputs. By combining audio signal processing and natural language processing technologies, the MultiSpec-DNN model can extract and analyze the comprehensive emotional characteristics in music files, thereby achieving more accurate emotion classification. In the experimental section, the MultiSpec-DNN model was tested on two standard emotional speech databases: EmoDB and IEMOCAP. The experimental results show that the MultiSpec-DNN model has a significant improvement in accuracy compared to traditional single-modal recognition methods, which proves the effectiveness of integrated features in emotion recognition.

Zhao Hanbing, Jin Xin and Guo Jinfeng. “Music Emotion Recognition and Analysis Based on Neural Network”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.3 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160382

@article{Hanbing2025,
title = {Music Emotion Recognition and Analysis Based on Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160382},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160382},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {3},
author = {Zhao Hanbing and Jin Xin and Guo Jinfeng}
}