The AMG1608 dataset for music emotion recognition
Yuan Chen, Yi‐Hsuan Yang, Ju-Chiang Wang, Homer H. Chen · 2015
Automated recognition of musical emotion from audio signals has received considerable attention recently. To construct an accurate model for music emotion prediction, the emotion-annotated music corpus has to be of high quality. It is desirable to have a large number of songs annotated by numerous subjects to characterize the general emotional response to a song. Due to the need for personalization of the music emotion prediction model to address the subjective nature of emotion perception, it is also important to have a large number of annotations per subject for training and evaluating a personalization method. In this paper, we discuss the deficiency of existing datasets and present a new one. The new dataset, which is publically available to the research community, is composed of 1608 30-second music clips annotated by 665 subjects. Furthermore, 46 subjects annotated more than 150 songs, making this dataset the largest of its kind to date.