  Pronounced as "musician", the musicnn library contains a set of pre-trained
musically motivated convolutional neural networks for music audio tagging:
https://github.com/jordipons/musicnn. This repository also includes some
pre-trained vgg-like baselines. These models can be used as out-of-the-box
music audio taggers, as music feature extractors, or as pre-trained models for
transfer learning.
  We also provide the code to train the aforementioned models:
https://github.com/jordipons/musicnn-training. This framework also allows
implementing novel models. For example, a musically motivated convolutional
neural network with an attention-based output layer (instead of the temporal
pooling 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.
