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Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition
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This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with the state-of-the-art method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the 'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.
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Personalized Dynamic Music Emotion Recognition with Dual-Scale Attention-Based Meta-Learning
DSAML uses dual-scale attention and meta-learning over per-annotator tasks to predict dynamic music emotion for both the average listener and for individual listeners from a single personalized song annotation.
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