Music Emotion Intensity Estimation using Transfer Ordinal Label Learning under Heterogeneous Scenes

Music emotion recognition plays an important role in many applications such as music material library construction and music recommendation system. The current music emotion recognition mainly focuses on discrete emotions or continuous emotions under single scene. However, on the one hand, intensity is one of important aspects of emotion, which can be represented as emotion rank or ordinal class. On the other hand, there may be not enough music emotion data in training set, which needs to transfer music emotion recognition model learnt from the music data in a source domain. The distribution of existing music data in target domain may differ from target music dataset. In order to overcome these two issues, this paper proposes to utilize transfer ordinal label learning (TOLL) to estimate music emotion. Compared with the previous works, TOLL-based music emotion intensity estimation implements music intensity estimation through transferring the knowledge in the existing source domain to unknown target domain. The experiments on several datasets show that TOLL can achieve promising results for emotion intensity estimation in single scene or across different scenes.