  Music emotion recognition (MER) is usually regarded as a multi-label tagging
task, and each segment of music can inspire specific emotion tags. Most
researchers extract acoustic features from music and explore the relations
between these features and their corresponding emotion tags. Considering the
inconsistency of emotions inspired by the same music segment for human beings,
seeking for the key acoustic features that really affect on emotions is really
a challenging task. In this paper, we propose a novel MER method by using deep
convolutional neural network (CNN) on the music spectrograms that contains both
the original time and frequency domain information. By the proposed method, no
additional effort on extracting specific features required, which is left to
the training procedure of the CNN model. Experiments are conducted on the
standard CAL500 and CAL500exp dataset. Results show that, for both datasets,
the proposed method outperforms state-of-the-art methods.
