Review of data features-based music emotion recognition methods

The ability of music to induce or convey emotions ensures the importance of its role in human life. Consequently, research on methods for identifying the high-level emotion states of a music segment from its low-level features has attracted attention. This paper offers new insights on music emotion recognition methods based on different combinations of data features that they use during the modeling phase from three aspects, music features only, ground-truth data only, and their combination, and provides a comprehensive review of them. Then, focusing on the relatively popular methods in which the two types of data, music features and ground-truth data, are combined, we further subdivide the methods in the literature according to the label- and numerical-type ground-truth data, and analyze the development of music emotion recognition with the cue of modeling methods and time sequence. Three current important research directions are then summarized. Although much has been achieved in the area of music emotion recognition, many issues remain. We review these issues and put forward some suggestions for future work.