tempocnn 0.0.5.dev0

Python audio signal processing library for musical tempo detection

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

DeepTemp Models

To use one of the DeepTemp models from [3] (see also repo directional_cnns), run

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

or,

tempo -m deeptemp_k24 --jams -i my_audio.wav

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

Mazurka Models

To use DT-Maz models from [4], run

tempo -m mazurka -i my_audio.wav

This defaults to the model named dt_maz_v_fold0. You may choose another fold [0-4] or another split [v|m]. So to use fold 3 from the M-split, use

tempo -m dt_maz_m_fold3 -i my_audio.wav

Batch Processing

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

find /your_audio_dir/ -name '*.wav' -print0 | xargs -0 tempo -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.

Interpolation

To increase accuracy for greater than integer-precision, you may want to enable quadratic interpolation. You can do so by setting the --interpolate flag. Obviously, this only makes sense for tracks with a very stable tempo:

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

Tempogram

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.

You may use the --csv option to export local tempo 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.

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

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

Original publication:

@inproceedings{SchreiberM18_TempoCNN_ISMIR,
   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},
   doi = {10.5281/zenodo.1492353},
   url = {https://doi.org/10.5281/zenodo.1492353}
}

ShallowTemp, DeepTemp, 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}
}

Mazurka models:

@inproceedings{SchreiberZM20_LocalTempo_ISMIR,
   Title = {Modeling and Estimating Local Tempo: A Case Study on Chopin’s Mazurkas},
   Author = {Hendrik Schreiber and Frank Zalkow and Meinard M{\"u}ller},
   Booktitle = {Proceedings of the 21th International Society for Music Information Retrieval Conference ({ISMIR})},
   Year = {2020},
   Address = {Montreal, QC, Canada}
}

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.

[3]

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.

[4]

Hendrik Schreiber, Frank Zalkow, Meinard Müller, Modeling and Estimating Local Tempo: A Case Study on Chopin’s Mazurkas, Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR), Montréal, QC, Canada, Oct. 2020.

Changes

0.0.5:

• Moved to TensorFlow 1.15.4.

• Consolidated version info.

• Consolidated requirements.

• Switched to pytest.

• Officially support Python 3.7.

• Enabled GitHub actions for packaging and testing.

• Added Pypi workflow.

• Cache models locally.

• Load models from GitHub.

0.0.4:

• Added support for DeepTemp, DeepSquare, and ShallowTemp models.

• Added support for Mazurka models.

• Added support for exporting data from tempograms.

• Added support for framewise normalization in tempograms.

• Moved to TensorFlow 1.15.2.

• Print number of model parameters.

0.0.3:

• Added flag --interpolate for tempo to increase accuracy.

• Migrated models to TensorFlow 1.10.1.

0.0.2:

• Added -d option for improved batch processing (tempo)

• Improved jams output

• Moved to librosa 0.6.2

• Continue processing batch, even when encountering an error

0.0.1:

• Initial version