HCMUS at MediaEval 2020: Emotion Classification Using Wavenet Features with SpecAugment and EfficientNet

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MediaEval 2020 provided a subset of the MTG-Jamendo dataset, aimed to recognize mood and theme in music. Team HCMUS proposes several solutions to build efficient classifiers to solve this problem. In addition to the mel-spectrogram features, new features extracted from the wavenet model is extracted and utilized to train the EfficientNet model. As evaluated by the jury, our best result achieved of 0.142 in PR-AUC and 0.76 in the ROC-AUC measurement. With fast training and lightweight features, our proposed methods are potential to work well with deeper neural networks.