Madmom is an audio signal processing library written in Python with a strong focus on music information retrieval (MIR) tasks.
The library is internally used by the Department of Computational Perception, Johannes Kepler University, Linz, Austria (http://www.cp.jku.at) and the Austrian Research Institute for Artificial Intelligence (OFAI), Vienna, Austria (http://www.ofai.at).
Possible acronyms are:
- Madmom Analyzes Digitized Music Of Musicians
- Mostly Audio / Dominantly Music Oriented Modules
It includes reference implementations for some music information retrieval algorithms, please see the References section.
Documentation of the package can be found online http://madmom.readthedocs.org
The package has two licenses, one for source code and one for model/data files.
Unless indicated otherwise, all source code files are published under the BSD license. For details, please see the LICENSE file.
Unless indicated otherwise, all model and data files are distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.
If you want to include any of these files (or a variation or modification thereof) or technology which utilises them in a commercial product, please contact Gerhard Widmer.
Please do not try to install from the .zip files provided by GitHub. Rather install it from package (if you just want to use it) or source (if you plan to use it for development) by following the instructions below. Whichever variant you choose, please make sure that all prerequisites are installed.
To install the madmom package, you must have either Python 2.7 or Python
3.5 or newer and the following packages installed:
In order to test your installation, process live audio input, or have improved FFT performance, additionally install these packages:
If you need support for audio files other than .wav with a sample rate of
44.1kHz and 16 bit depth, you need ffmpeg (avconv on Ubuntu Linux has
some decoding bugs, so we advise not to use it!).
Please refer to the requirements.txt file for the minimum required versions and make sure that these modules are up to date, otherwise it can result in unexpected errors or false computations!
The instructions given here should be used if you just want to install the package, e.g. to run the bundled programs or use some functionality for your own project. If you intend to change anything within the madmom package, please follow the steps in the next section.
The easiest way to install the package is via pip from the PyPI (Python
Package Index):
pip install madmom
This includes the latest code and trained models and will install all dependencies automatically.
You might need higher privileges (use su or sudo) to install the package, model
files and scripts globally. Alternatively you can install the package locally
(i.e. only for you) by adding the --user argument:
pip install --user madmom
This will also install the executable programs to a common place (e.g.
/usr/local/bin), which should be in your $PATH already. If you
installed the package locally, the programs will be copied to a folder which
might not be included in your $PATH (e.g. ~/Library/Python/2.7/bin
on Mac OS X or ~/.local/bin on Ubuntu Linux, pip will tell you). Thus
the programs need to be called explicitely or you can add their install path
to your $PATH environment variable:
export PATH='path/to/scripts':$PATH
If you plan to use the package as a developer, clone the Git repository:
git clone --recursive https://github.com/CPJKU/madmom.git
Since the pre-trained model/data files are not included in this repository but rather added as a Git submodule, you either have to clone the repo recursively. This is equivalent to these steps:
git clone https://github.com/CPJKU/madmom.git cd madmom git submodule update --init --remote
Then you can simply install the package in development mode:
python setup.py develop --user
To run the included tests:
python setup.py pytest
To upgrade the package, please use the same mechanism (pip vs. source) as you did for installation. If you want to change from package to source, please uninstall the package first.
Simply upgrade the package via pip:
pip install --upgrade madmom [--user]
If some of the provided programs or models changed (please refer to the CHANGELOG) you should first uninstall the package and then reinstall:
pip uninstall madmom pip install madmom [--user]
Simply pull the latest sources:
git pull
To update the models contained in the submodule:
git submodule update
If any of the .pyx or .pxd files changed, you have to recompile the
modules with Cython:
python setup.py build_ext --inplace
The package has a very simple structure, divided into the following folders:
- /bin
- this folder includes example programs (i.e. executable algorithms)
- /docs
- package documentation
- /madmom
- the actual Python package
- /madmom/audio
- low level features (e.g. audio file handling, STFT)
- /madmom/evaluation
- evaluation code
- /madmom/features
- higher level features (e.g. onsets, beats)
- /madmom/ml
- machine learning stuff (e.g. RNNs, HMMs)
- /madmom/models
- pre-trained model/data files (see the License section)
- /madmom/utils
- misc stuff (e.g. MIDI and general file handling)
- /tests
- tests
The package includes executable programs in the /bin folder. If you installed the package, they were copied to a common place.
All scripts can be run in different modes: in single file mode to process
a single audio file and write the output to STDOUT or the given output file:
DBNBeatTracker single [-o OUTFILE] INFILE
If multiple audio files should be processed, the scripts can also be run in
batch mode to write the outputs to files with the given suffix:
DBNBeatTracker batch [-o OUTPUT_DIR] [-s OUTPUT_SUFFIX] FILES
If no output directory is given, the program writes the output files to the same location as the audio files.
Some programs can also be run in online mode, i.e. operate on live audio
signals. This requires pyaudio
to be installed:
DBNBeatTracker online [-o OUTFILE] [INFILE]
The pickle mode can be used to store the used parameters to be able to
exactly reproduce experiments.
Please note that the program itself as well as the modes have help messages:
DBNBeatTracker -h DBNBeatTracker single -h DBNBeatTracker batch -h DBNBeatTracker online -h DBNBeatTracker pickle -h
will give different help messages.
The mailing list should be used to get in touch with the developers and other users.
The wiki can be found here: https://github.com/CPJKU/madmom/wiki
Frequently asked questions can be found here: https://github.com/CPJKU/madmom/wiki/FAQ
If you use madmom in your work, please consider citing it:
@inproceedings{madmom,
Title = {{madmom: a new Python Audio and Music Signal Processing Library}},
Author = {B{\"o}ck, Sebastian and Korzeniowski, Filip and Schl{\"u}ter, Jan and Krebs, Florian and Widmer, Gerhard},
Booktitle = {Proceedings of the 24th ACM International Conference on
Multimedia},
Month = {10},
Year = {2016},
Pages = {1174--1178},
Address = {Amsterdam, The Netherlands},
Doi = {10.1145/2964284.2973795}
}| [1] | Florian Eyben, Sebastian Böck, Björn Schuller and Alex Graves, Universal Onset Detection with bidirectional Long Short-Term Memory Neural Networks, Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR), 2010. |
| [2] | Sebastian Böck and Markus Schedl, Enhanced Beat Tracking with Context-Aware Neural Networks, Proceedings of the 14th International Conference on Digital Audio Effects (DAFx), 2011. |
| [3] | Sebastian Böck and Markus Schedl, Polyphonic Piano Note Transcription with Recurrent Neural Networks, Proceedings of the 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012. |
| [4] | Sebastian Böck, Andreas Arzt, Florian Krebs and Markus Schedl, Online Real-time Onset Detection with Recurrent Neural Networks, Proceedings of the 15th International Conference on Digital Audio Effects (DAFx), 2012. |
| [5] | Sebastian Böck, Florian Krebs and Markus Schedl, Evaluating the Online Capabilities of Onset Detection Methods, Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR), 2012. |
| [6] | Sebastian Böck and Gerhard Widmer, Maximum Filter Vibrato Suppression for Onset Detection, Proceedings of the 16th International Conference on Digital Audio Effects (DAFx), 2013. |
| [7] | Sebastian Böck and Gerhard Widmer, Local Group Delay based Vibrato and Tremolo Suppression for Onset Detection, Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR), 2013. |
| [8] | Florian Krebs, Sebastian Böck and Gerhard Widmer, Rhythmic Pattern Modelling for Beat and Downbeat Tracking in Musical Audio, Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR), 2013. |
| [9] | Sebastian Böck, Jan Schlüter and Gerhard Widmer, Enhanced Peak Picking for Onset Detection with Recurrent Neural Networks, Proceedings of the 6th International Workshop on Machine Learning and Music (MML), 2013. |
| [10] | Sebastian Böck, Florian Krebs and Gerhard Widmer, A Multi-Model Approach to Beat Tracking Considering Heterogeneous Music Styles, Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR), 2014. |
| [11] | Filip Korzeniowski, Sebastian Böck and Gerhard Widmer, Probabilistic Extraction of Beat Positions from a Beat Activation Function, Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR), 2014. |
| [12] | Sebastian Böck, Florian Krebs and Gerhard Widmer, Accurate Tempo Estimation based on Recurrent Neural Networks and Resonating Comb Filters, Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), 2015. |
| [13] | Florian Krebs, Sebastian Böck and Gerhard Widmer, An Efficient State Space Model for Joint Tempo and Meter Tracking, Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), 2015. |
| [14] | Sebastian Böck, Florian Krebs and Gerhard Widmer, Joint Beat and Downbeat Tracking with Recurrent Neural Networks, Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), 2016. |
| [15] | Filip Korzeniowski and Gerhard Widmer, Feature Learning for Chord Recognition: The Deep Chroma Extractor, Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), 2016. |
| [16] | Florian Krebs, Sebastian Böck, Matthias Dorfer and Gerhard Widmer, Downbeat Tracking Using Beat-Synchronous Features and Recurrent Networks, Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), 2016. |
| [17] | Filip Korzeniowski and Gerhard Widmer, A Fully Convolutional Deep Auditory Model for Musical Chord Recognition, Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2016. |
| [18] | Filip Korzeniowski and Gerhard Widmer, Genre-Agnostic Key Classification with Convolutional Neural Networks, Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018. |
| [19] | Rainer Kelz, Sebastian Böck and Gerhard Widmer, Deep Polyphonic ADSR Piano Note Transcription, Proceedings of the 44th International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019. |
Supported by the European Commission through the GiantSteps project (FP7 grant agreement no. 610591) and the Phenicx project (FP7 grant agreement no. 601166) as well as the Austrian Science Fund (FWF) project Z159.
