transfer_learning_music

by keunwoochoi

Transfer learning for music classification and regression tasks

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transferlearningmusic

Repo for paper "Transfer learning for music classification and regression tasks" by Keunwoo Choi et al.

diagram results

Mode 1/2. To use the pre-trained convnet feature extractor

For your own music/audio-related work.

Prerequisites (Same as mode 2 except datasets)

$ pip install theano==0.9
$ pip install keras==1.2.2
$ git clone https://github.com/keunwoochoi/kapre.git
$ cd kapre
$ git checkout a3bde3e
$ python setup.py install

Usage

$ python easy_feature_extraction.py audio_paths.txt some/path/features.npy

where

audio_path.txt
is line-by-line audio paths and
some/path/features.npy
is the path to save the result.

E.g.,

audio_path.txt
:
blah/a.mp3
blahblah/234.wav
some/other.c.mp3

Then load the

.npy
file. The features are size of
(num_songs, 160)
.

Mode 2/2. To reproduce the paper

Prerequisites

$ git clone https://github.com/keunwoochoi/kapre.git
$ cd kapre
$ git checkout a3bde3e
$ python setup.py install
  • Optionally,
    Sckikt learn, Pandas, Numpy
    ,.. for your convenience.

Usage

  • 0. main_prepare_many_datasets.ipynb
    : prepare dataset, pre-processing
  • 1. feature extraction for 6 tasks.ipynb
    : feature extraction (MFCC and convnet features)
  • 2_main_knn_svm_transfer
    : Do SVM
  • 3. knn and svm (with AveragePooling) results plots
    : Plot results

Appendix

Links

Citation:

@inproceedings{choi2017transfer,
  title={Transfer learning for music classification and regression tasks},
  author={Choi, Keunwoo and Fazekas, George and Sandler, Mark and Cho, Kyunghyun},
  booktitle={The 18th International Society of Music Information Retrieval (ISMIR) Conference 2017, Suzhou, China},
  year={2017},
  organization={International Society of Music Information Retrieval}
}

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