Xiaoxiao Yu
- W. Han , "Semi-supervised active learning for sound classification in hybrid learning environments," PloS One, vol. 11, (9), pp. e0162075-e0162075, 2016.Google Scholar
Cross Ref
- S. Pouyanfar and H. Sameti, "Music emotion recognition using two level classification," in 2014, . DOI: 10.1109/IranianCIS.2014.6802519.Google ScholarCross Ref
- M. Wang, N. Zhang, H. Zhu.User-adaptive Music Emotion Recognition[J].New York,NY,10016-5997,United States,2004:201-209.Google Scholar
- S. Tong and D. Koller. Support vector machine active learning with applications to text classification. The Journal of Machine Learning Research, 2:45–66, 2002.Google ScholarDigital Library
- J. Zhang, R. Chan, and P. Fung. Extractive speech summarization by active learning. In Automatic Speech Recognition & Understanding, 2009. ASRU 2009. IEEE Workshop on, pages 392–397. IEEE, 2009.Google Scholar
- D. Su and P. Fung, "Personalized music emotion classification via active learning," in 2012, . DOI: 10.1145/2390848.2390864.Google ScholarDigital Library
- Z. Liu, Q. Li, S. Yang, Y. Deng, F. Liu, Xinming. Lai and Xueke. Bai, "An Sentiment Analysis Model Based on Active Learning and Multiple Supervised Learning", in 2020, DOI: 10.3969/j.issn.1673-5692.Google Scholar
- R. E. Thayer, The Biopsychology of Mood and Arousal. (Reprint ed.) 1990;1991;.Google Scholar
- A. Dey, "Machine Learning Algorithms: A Review", International Journal of Computer Science and Information Technologies, Vol. 7 (3) , 2016, 1174-1179Google Scholar
- P. Harrington, “Machine Learning in Action”, Manning Publications Co., Shelter Island, New York, ISBN 9781617290183, 2012Google ScholarDigital Library
- R. v. Appen and M. Frei-Hauenschild, "AABA, Refrain, Chorus, Bridge, Prechorus Song Forms and Their Historical Development," German Society for Popular Music Studies, vol. 10.3.2015, no. 13, 2015.Google Scholar
- D. Cabrera Psysound: A computer program for psychoacoustical analysis. In Proceedings of the Australian Acoustical Society Conference, volume 24, pages 47–54, 1999.Google Scholar
- F. Eyben , "Recent developments in openSMILE, the munich open-source multimedia feature extractor," in 2013, . DOI: 10.1145/2502081.2502224.Google ScholarDigital Library
- L. Lu, H. Zhang and S. Z. Li, "Content-based audio classification and segmentation by using support vector machines," Multimedia Systems, vol. 8, (6), pp. 482-492, 2003.Google ScholarCross Ref
- S. Tong and E. Chang. Support vector machine active learning for image retrieval. In proceedings of the ninth ACM international conference on Multimedia, pages 107–118. ACM, 2001.Google Scholar
Recommendations
Machine Recognition of Music Emotion: A Review
The proliferation of MP3 players and the exploding amount of digital music content call for novel ways of music organization and retrieval to meet the ever-increasing demand for easy and effective information access. As almost every music piece is ...
Music emotion recognition based on a modified brain emotional learning model
AbstractListening to music can evoke different emotions in humans. Music emotion recognition (MER) can predict a person’s emotions before listening to a song. However, there are three problems with MER studies. First, the brain is the seat of music ...
The Role of Time in Music Emotion Recognition: Modeling Musical Emotions from Time-Varying Music Features
CMMR 2012: Revised Selected Papers of the 9th International Symposium on From Sounds to Music and Emotions - Volume 7900Music is widely perceived as expressive of emotion. However, there is no consensus on which factors in music contribute to the expression of emotions, making it difficult to find robust objective predictors for music emotion recognition MER. Currently, ...
Comments