Awesome

AuralisationCNN

This repo is for an example of auralisastion of CNNs that is demonstrated on ISMIR 2015.

Files

auralise.py: includes all required function for deconvolution. example.py: includes the whole code - just clone and run it by python example.py You might need to use older version of Keras, e.g. this (ver 0.3.x)

Folders

src_songs: includes three songs that I used in my blog posting.

Usage

Load weights that you want to auralise. I'm using this function W = load_weights() to load my keras model, it can be anything else. W is a list of weights for the convnet. (TODO: more details)

Then load source files, get STFT of it. I'm using librosa.

Then deconve it with get_deconve_mask.

Citation

This paper, or simply,

@inproceedings{choi2015auralisation,
  title={Auralisation of Deep Convolutional Neural Networks: Listening to Learned Features},
  author={Choi, Keunwoo and Kim, Jeonghee and Fazekas, George and Sandler, Mark},
  booktitle={International Society of Music Information Retrieval (ISMIR), Late-Breaking/Demo Session, New York, USA},
  year={2015},
  organization={International Society of Music Information Retrieval}
}

External links

• The second blog post has more extensive demo. Detailed description will follow after paper submission.
• The first blog post that explains my ISMIR 2015 Late-Breaking session paper.

Credits

• Keras, librosa, [Matt's deconvolution] paper(http://arxiv.org/abs/1311.2901), Naver Labs