Acceso abierto

Emotional Characterization Mining in Music Performance and Its Guiding Role


Cite

Hsu, J. L., Zhen, Y. L., Lin, T. C., & Chiu, Y. S. (2017). Affective content analysis of music emotion through eeg. Multimedia Systems, 24(1), 1–16. Search in Google Scholar

Xiang, Y. (2022). Computer analysis and automatic recognition technology of music emotion. Mathematical Problems in Engineering, 2022. Search in Google Scholar

Chau, C. J., Gilburt, S. J. M., Mo, R., & Horner, A. (2017). The emotional characteristics of bowed string instruments with different pitch and dynamics. Audio Engineering Society. Search in Google Scholar

An, Y., Sun, S., & Wang, S. (2017). Naive Bayes Classifiers for Music Emotion Classification Based on Lyrics. International Conference on Computer and Information Science. Search in Google Scholar

Hu, Y. (2022). Music emotion research based on reinforcement learning and multimodal information. Journal of Mathematics, 2022. Search in Google Scholar

Xu, K. (2020). Establishment of music emotion model based on blockchain network environment. Wireless Communications and Mobile Computing. Search in Google Scholar

Takashima, N., Li, Frédéric, Grzegorzek, M., & Shirahama, K. (2021). Cross-modal music emotion recognition using composite loss-based embeddings. arXiv e-prints. Search in Google Scholar

Lopes, P. S., Lasmar, E. L., Rosa, R. L., & Demostenes Z. Rodríguez. (2018). The Use of the Convolutional Neural Network as an Emotion Classifier in a Music Recommendation System. the XIV Brazilian Symposium. Search in Google Scholar

Seo, Y. S., & Huh, J. H. (2019). Automatic emotion-based music classification for supporting intelligent iot applications. Electronics, 8(2). Search in Google Scholar

Choi, J. H., & Chong, H. J. (2021). An explorative study on the perceived emotion of music: according to cognitive styles of music listening. The Journal of the Acoustical Society of Korea. Search in Google Scholar

Su, J., & Zhou, P. (2022). Machine learning-based modeling and prediction of the intrinsic relationship between human emotion and music. ACM transactions on applied perception. Search in Google Scholar

Chen, C., & Li, Q. (2020). A multimodal music emotion classification method based on multifeature combined network classifier. Mathematical Problems in Engineering, 2020, 1–11. Search in Google Scholar

Yang, S., Reed, C. N., Chew, E., & Barthet, M. (2021). Examining emotion perception agreement in live music performance. IEEE Transactions on Affective Computing, PP(99), 1–1. Search in Google Scholar

Hong, Y., Chau, C. J., & Horner, A. B. (2017). An analysis of low-arousal piano music ratings to uncover what makes calm and sad music so difficult to distinguish in music emotion recognition. Journal of the Audio Engineering Society: Audio, Acoustics, Applications(4). Search in Google Scholar

Hasanzadeh, F., Annabestani, M., & Moghimi, S. (2021). Continuous emotion recognition during music listening using eeg signals: a fuzzy parallel cascades model. Elsevier. Search in Google Scholar

Rosli, N., Rajaee, N., & Bong, D. (2018). Renica based music source separation for automatic music emotion classification. International journal of innovative computing, information and control(14–6). Search in Google Scholar

Dong, Y., Yang, X., Zhao, X., & Li, J. (2019). Bidirectional convolutional recurrent sparse network (bcrsn): an efficient model for music emotion recognition. IEEE Transactions on Multimedia, 21(12), 3150–3163. Search in Google Scholar

Wani, V., Bothe, N., & Soni, A. (2021). Music suggestion via sentimental analysis of user-inputted texts. International Journal of Scientific Research in Computer Science Engineering and Information Technology. Search in Google Scholar

Sorussa, K., Choksuriwong, A., & Karnjanadecha, M. (2020). Emotion classification system for digital music with a cascaded technique. ECTI Transactions on Computer and Information Technology (ECTICIT), 14(1), 53–66. Search in Google Scholar

Grekow, J. (2018). From content-based music emotion recognition to emotion maps of musical pieces. Studies in Computational Intelligence. Search in Google Scholar

Byun, S. W., Lee, S. P., & Han, H. S. (2017). Feature selection and comparison for the emotion recognition according to music listening. IEEE. Search in Google Scholar

eISSN:
2444-8656
Idioma:
Inglés
Calendario de la edición:
Volume Open
Temas de la revista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics