Temporal Pooling and Multiscale Learning for Automatic Annotation and Ranking of Music Audio

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EmoNets: Multimodal deep learning approaches for emotion recognition in video (2015, methodology): “Online PCA was applied on the extracted features for whitening [16].”
Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms (2017, methodology): “he depth of layers and the stride of ﬁrst convolution layer. Third, in music classiﬁcation tasks, the input size of the network is an important parameter that determines the classiﬁcation performance [16,17]. In the mel-spectrogram model, one song is generally divided into small segments with 1 to 4”
Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging (2017, background): “Recently, as convolutional neural network (CNN) has become the de-facto standard in image classification, the deep learning approach has been actively explored in music auto-tagging as well as by using the spectrogram and its variants as input data and so recasting it as a multi-label classification”
Representation Learning: A Review and New Perspectives (2012, background): “Deep learning also helped to win MIREX (Music Information Retrieval) competitions, e.g. in 2011 on audio tagging (Hamel et al., 2011).”