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Pronounced as "musician", the musicnn library contains a set of pre-trainedmusically motivated convolutional neural networks for music audio tagging:https://github.com/jordipons/musicnn. This repository also includes somepre-trained vgg-like baselines. These models can be used as out-of-the-boxmusic audio taggers, as music feature extractors, or as pre-trained models fortransfer learning.  We also provide the code to train the aforementioned models:https://github.com/jordipons/musicnn-training. This framework also allowsimplementing novel models. For example, a musically motivated convolutionalneural network with an attention-based output layer (instead of the temporalpooling layer) can achieve state-of-the-art results for music audio tagging:90.77 ROC-AUC / 38.61 PR-AUC on the MagnaTagATune dataset --- and 88.81 ROC-AUC/ 31.51 PR-AUC on the Million Song Dataset.

musicnn: Pre-trained convolutional neural networks for music audio tagging