musicnn: Pre-trained convolutional neural networks for music audio tagging
Abstract
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.
Keywords
Cite
@article{arxiv.1909.06654,
title = {musicnn: Pre-trained convolutional neural networks for music audio tagging},
author = {Jordi Pons and Xavier Serra},
journal= {arXiv preprint arXiv:1909.06654},
year = {2019}
}
Comments
Accepted to be presented at the Late-Breaking/Demo session of ISMIR 2019