Multi-scale Context Based Attention for Dynamic Music Emotion Prediction
Ye Ma, XinXing Li, Mingxing Xu, Jia Jia, Lianhong Cai · 2017
Dynamic music emotion prediction is to recognize the continuous emotion information in music, which is necessary for music retrieval and recommendation. In this paper, we adopt the dimensional valence-arousal (V-A) emotion model to represent the dynamic emotion in music. In our opinion, music and V-A emotion label do not have the one-to-one correspondence in the time domain, while the expression of music emotion at one moment is the accumulation of previous music content for a period of time, so we propose Long Short-Term Memory (LSTM) based sequence-to-one mapping for dynamic music emotion prediction. Based on this sequence-to-one music emotion mapping, it is proved that different time scales' preceding content has an influence on the LSTM model's performance, so we further propose the Multi-scale Context based Attention (MCA) for dynamic music emotion prediction. We evaluate our proposed method on the database of Emotion in Music task at MediaEval 2015, and the results show that our proposed method outperforms most of the models using the same features and achieves a competitive performance with the state-of-the-art methods.