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

September 14, 2019 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .gitignore, DOCUMENTATION.md, FAQs.md, LICENSE.md, MANIFEST.in, README.md, audio, images, musicnn, musicnn_example.ipynb, setup.py, tagging_example.ipynb, vgg_example.ipynb

Authors Jordi Pons, Xavier Serra arXiv ID 1909.06654 Category cs.SD: Sound Cross-listed cs.CL, eess.AS Citations 114 Venue arXiv.org Repository https://github.com/jordipons/musicnn ⭐ 678 Last Checked 2 months ago

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.