The AMG1608 dataset for music emotion recognition

Abstract

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.