Framework for estimating temporal properties of music tracks.
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README.rst

Tempo-CNN

Tempo-CNN is a simple CNN-based framework for estimating temporal properties of music tracks.

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

tempo -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 the model originally used in the ISMIR 2018 paper [1], please run

tempo -m ismir2018 --jams -i my_audio.wav

Instead of estimating a global tempo, Tempo-CNN can also estimate local tempi in the form of a tempogram. This can be useful for identifying tempo drift. To create such a tempogram, run

tempogram -p my_audio.wav

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

Tempo-CNN provides experimental support for temporal property estimation of Greek folk music [2]. The corresponding models are named fma2018 (for tempo) and fma2018-meter (for meter). To estimate the meter's numerator, run

meter -m fma2018-meter -i my_audio.wav

Installation

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

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

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 Tempo-CNN in your work, please consider citing it:

@inproceedings{
   Title = {A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network},
   Author = {Schreiber, Hendrik and M{\"u}ller Meinard},
   Booktitle = {Proceedings of the 19th International Society for Music Information Retrieval Conference ({ISMIR})},
   Pages = {98--105},
   Month = {9},
   Year = {2018},
   Address = {Paris, France}
}

References

[1]Hendrik Schreiber, Meinard Müller, A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, Sept. 2018.
[2]Hendrik Schreiber, Technical Report: Tempo and Meter Estimation for Greek Folk Music Using Convolutional Neural Networks and Transfer Learning 8th International Workshop on Folk Music Analysis (FMA), Thessaloniki, Greece, June 2018.