  Music emotion recognition (MER), a sub-task of music information retrieval
(MIR), has developed rapidly in recent years. However, the learning of
affect-salient features remains a challenge. In this paper, we propose an
end-to-end attention-based deep feature fusion (ADFF) approach for MER. Only
taking log Mel-spectrogram as input, this method uses adapted VGGNet as spatial
feature learning module (SFLM) to obtain spatial features across different
levels. Then, these features are fed into squeeze-and-excitation (SE)
attention-based temporal feature learning module (TFLM) to get multi-level
emotion-related spatial-temporal features (ESTFs), which can discriminate
emotions well in the final emotion space. In addition, a novel data processing
is devised to cut the single-channel input into multi-channel to improve
calculative efficiency while ensuring the quality of MER. Experiments show that
our proposed method achieves 10.43% and 4.82% relative improvement of valence
and arousal respectively on the R2 score compared to the state-of-the-art
model, meanwhile, performs better on datasets with distinct scales and in
multi-task learning.
