Key-CNN

Key-CNN is a simple CNN-based framework for estimating harmonic properties of music tracks.

First and foremost, Key-CNN is a key estimator. To determine the global key of an audio file, simply run the script

key -i my_audio.wav

You may specify other models and output formats (mirex, JAMS) via command line parameters.

E.g. to create JAMS as output format and a deepsquare model used in the SMC paper [1], please run

key -m deepsquare --jams -i my_audio.wav

To use one of the DeepSpec models from [1] (see also repo directional_cnns), run

key -m deepspec --jams -i my_audio.wav

or,

key -m deepspec_k24 --jams -i my_audio.wav

if you want to use a higher capacity model (some k-values are supported). deepsquare and shallowspec models may also be used.

For estimation using models trained for [2], you may run one of the following model specs:

key -m winterreise -i my_audio.wav
key -m winterreise_v -i my_audio.wav
key -m winterreise_s -i my_audio.wav
key -m winterreise_v_fold0 -i my_audio.wav
key -m winterreise_s_fold1 -i my_audio.wav

For more model names and split training split definitions, please see the models directory in the GitHub repo (just remove the .h5 from the file name to use as model name). The groundtruth annotations for Winterreise models may be found here.

For batch processing, you may want to run key like this:

find /your_audio_dir/ -name '*.wav' -print0 | xargs -0 key -d /output_dir/ -i

This will recursively search for all .wav files in /your_audio_dir/, analyze then and write the results to individual files in /output_dir/. Because the model is only loaded once, this method of processing is much faster than individual program starts.

Instead of estimating a global key, Key-CNN can also estimate local keys in the form of a keygram. This can be useful for identifying modulations. To create such a keygram, run

keygram -p my_audio.wav

As output, keygram will create a .png file. Additional options to select different models and output formats are available.

You may use the --csv option to export local key estimates in a parseable format and the --hop-length option to change temporal resolution. The parameters --sharpen and --norm-frame let you post-process the image.

Installation

Clone this repo and run setup.py install using Python 3.6:

git clone https://github.com/hendriks73/key-cnn.git
cd key-cnn
python setup.py install

You may need to install TensorFlow using pip from the command line.

License

Source code and models can be licensed under the GNU AFFERO GENERAL PUBLIC LICENSE v3. For details, please see the LICENSE file.

Citation

If you use Key-CNN in your work, please consider citing it. ShallowSpec, DeepSpec, and DeepSquare models:

@inproceedings{SchreiberM19_CNNKeyTempo_SMC,
   Title = {Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters},
   Author = {Hendrik Schreiber and Meinard M{\"u}ller},
   Booktitle = {Proceedings of the Sound and Music Computing Conference ({SMC})},
   Pages = {47--54},
   Year = {2019},
   Address = {M{\'a}laga, Spain}
}

All Winterreise [2] models and annotations:

@inproceedings{SchreiberWM20_HMMCNNLocalKey_ICASSP,
   Title = {Local Key Estimation in Classical Music Recordings: A Cross-Version Study on {Schubert's} {Winterreise}},
   Author = {Hendrik Schreiber, Christof Wei{\ss}, Meinard M{\"u}ller},
   Booktitle = {Proceedings of the {IEEE} International Conference on Acoustics, Speech, and Signal Processing ({ICASSP})},
   Year = {2020},
   Address = {Barcelona, Spain}
}

References

[1]	Hendrik Schreiber, Meinard Müller, Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional Filters Proceedings of the Sound and Music Computing Conference (SMC), Málaga, Spain, 2019.

[2]	Hendrik Schreiber, Christof Weiß, Meinard Müller, Local Key Estimation in Classical Music Recordings: A Cross-Version Study on Schubert's Winterreise. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, 2020.