A Regression Approach to Music Emotion Recognition

By Yi-Hsuan Yang, Yu-Ching Lin, Ya-Fan Su, Homer H. Chen. IEEE Transactions on Audio, Speech, and Language Processing, 2008

482 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f32c2b25c02c84a0691b04a6b250f95e057640df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ioannis P. Vlahavas
2. Grigorios Tsoumakas
3. Konstantinos Trohidis
4. Zhouyu Fu
5. Guojun Lu
6. George M. Kalliris
7. Dengsheng Zhang
8. Kai Ming Ting

Citations in the corpus

• EEG-based classification of positive and negative affective states (2014, background): “(all evaluated in [18]), to fractal-based approaches [4] and regression techniques [39].”
• Multi-Label Classification of Music into Emotions (2008, methodology): “Past approaches towards automated detection of emotions in music modeled the learning problem as a single-label classification [9, 20], regression [19], or multilabel classi-fication [6, 7, 17] task.”
• A Survey of Audio-Based Music Classification and Annotation (2011, methodology): “It has shown in many mood classification systems [25], [27], [75] that OSC is more preferable”