Learning emotion-based acoustic features with deep belief networks

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Deep Learning: Methods and Applications (2014, background): “In the general field of audio and music processing, the impacted areas by deep learning include mainly music signal processing and music information retrieval [15, 22, 141, 177, 178, 179, 319].”
Improving Content-based and Hybrid Music Recommendation using Deep Learning (2014, background): “In fact, people have already started using deep learning to learn features for other music tasks such as music genre classification [7] and music emotion prediction [8] with promising results.”
3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition (2018, methodology): “[2] employed a deep belief network to extract high-level emotional feature representations from the”
Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks (2013, methodology): “More recently, Kim et al. [22] used DBNs to capture non-linear feature interactions in audiovisual data and found improvement over baselines that did not use deep learning.”
Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks (2014, background): “Schmidt and Kim [33] used deep belief networks (DBNs) to learn high-level features directly frommagnitude spectra and achieved good performances on emotional music recognition compared to other feature extraction schemes.”