Skip to main content

Python audio signal processing library

Project description

======
madmom
======

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
=============

Documentation of the package can be found online http://madmom.readthedocs.org


License
=======

The package has two licenses, one for source code and one for model/data files.

Source code
-----------

Unless indicated otherwise, all source code files are published under the BSD
license. For details, please see the `LICENSE <LICENSE>`_ file.

Model and data files
--------------------

Unless indicated otherwise, all model and data files are distributed under the
`Creative Commons Attribution-NonCommercial-ShareAlike 4.0
<http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode>`_ 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 <http://www.cp.jku.at/people/widmer/>`_.


Installation
============

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.

Prerequisites
-------------

To install the ``madmom`` package, you must have either Python 2.7 or Python
3.3 or newer and the following packages installed:

- `numpy <http://www.numpy.org>`_
- `scipy <http://www.scipy.org>`_
- `cython <http://www.cython.org>`_
- `nose <https://github.com/nose-devs/nose>`_ (to run the tests)

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 <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!

Install from package
--------------------

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) <https://pypi.python.org/pypi>`_::

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

Install from source
-------------------

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 test

Upgrade of existing installations
---------------------------------

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.

Upgrade a package
~~~~~~~~~~~~~~~~~

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]

Upgrade from source
~~~~~~~~~~~~~~~~~~~

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

Package structure
-----------------

The package has a very simple structure, divided into the following folders:

`/bin <bin>`_
this folder includes example programs (i.e. executable algorithms)
`/docs <docs>`_
package documentation
`/madmom <madmom>`_
the actual Python package
`/madmom/audio <madmom/audio>`_
low level features (e.g. audio file handling, STFT)
`/madmom/evaluation <madmom/evaluation>`_
evaluation code
`/madmom/features <madmom/features>`_
higher level features (e.g. onsets, beats)
`/madmom/ml <madmom/ml>`_
machine learning stuff (e.g. RNNs, HMMs)
`/madmom/models <madmom/models>`_
pre-trained model/data files (see the License section)
`/madmom/utils <madmom/utils>`_
misc stuff (e.g. MIDI and general file handling)
`/tests <tests>`_
tests

Executable programs
-------------------

The package includes executable programs in the `/bin <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::

SuperFlux 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::

SuperFlux batch [-o OUTPUT_DIR] [-s OUTPUT_SUFFIX] LIST OF INPUT FILES

If no output directory is given, the program writes the output files to same
location as the audio files.

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::

SuperFlux -h

SuperFlux single -h

SuperFlux batch -h

SuperFlux pickle -h

will give different help messages.


Additional resources
====================

Mailing list
------------

The `mailing list <https://groups.google.com/d/forum/madmom-users>`_ should be
used to get in touch with the developers and other users.

Wiki
----

The wiki can be found here: https://github.com/CPJKU/madmom/wiki

FAQ
---

Frequently asked questions can be found here:
https://github.com/CPJKU/madmom/wiki/FAQ

References
==========

.. [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*,
In 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] 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.


Acknowledgements
================

Supported by the European Commission through the `GiantSteps project
<http://www.giantsteps-project.eu>`_ (FP7 grant agreement no. 610591) and the
`Phenicx project <http://phenicx.upf.edu>`_ (FP7 grant agreement no. 601166)
as well as the `Austrian Science Fund (FWF) <https://www.fwf.ac.at>`_ project
Z159.

Release Notes
=============

Version 0.14 (release date: 2016-07-28)
---------------------------------------

New features:

* Downbeat tracking based on Recurrent Neural Network (RNN) and Dynamic
Bayesian Network (DBN) (#130)
* Convolutional Neural Networks (CNN) and CNN onset detection (#133)
* Linear-Chain Conditional Random Field (CRF) implementation (#144)
* Deep Neural Network (DNN) based chroma vector extraction (#148)
* CRF chord recognition using DNN chroma vectors (#148)
* CNN chord recognition using CRF decoding (#152)
* Initial Windows support (Python 2.7 only, no pip packages yet) (#157)
* Gated Recurrent Unit (GRU) network layer (#167)

Bug fixes:

* Fix downbeat output bug (#128)
* MIDI file creation bug (#166)

API relevant changes:

* Refactored the `ml.rnn` to `ml.nn` and converted the models to pickles (#110)
* Reordered the dimensions of comb_filters to time, freq, tau (#135)
* `write_notes` uses `delimiter` instead of `sep` to separate columns (#155)
* `LSTMLayer` takes `Gate`s as arguments, all layers are callable (#161)
* Replaced `online` parameter of `FramedSignalProcessor` by `origin` (#169)

Other changes:

* Added classes for onset/note/beat detection with RNNs to `features.*` (#118)
* Add examples to docstrings of classes (#119)
* Converted `madmom.modules` into a Python package (#125)
* `match_files` can handle inexact matches (#137)
* Updated beat tracking models to MIREX 2015 ones (#146)
* Tempo and time signature can be set for created MIDI files (#166)


Version 0.13.2 (release date: 2016-06-09)
-----------------------------------------

This is a bugfix release.

* Fix custom filterbank in FilteredSpectrogram (#142)

Version 0.13.1 (release date: 2016-03-14)
-----------------------------------------

This is a bugfix release.

* Fix beat evaluation argument parsing (#116)

Version 0.13 (release date: 2016-03-07)
---------------------------------------

New features:

* Python 3 support (3.3+) (#15)
* Online documentation available at http://madmom.readthedocs.org (#60)

Bug fixes:

* Fix nasty unsigned indexing bug (#88)
* MIDI note timing could get corrupted if `note_ticks_to_beats()` was called
multiple times (#90)

API relevant changes:

* Renamed `DownBeatTracker` and all relevant classes to `PatternTracker` (#25)
* Complete refactoring of the `features.beats_hmm` module (#52)
* Unified negative index behaviour of `FramedSignal` (#72)
* Removed pickling of data classes since it was not tested thoroughly (#81)
* Reworked stacking of spectrogram differences (#82)
* Renamed `norm_bands` argument of `MultiBandSpectrogram` to `norm_filters`
(#83)

Other changes:

* Added alignment evaluation (#12)
* Added continuous integration testing (#16)
* Added `-o` option to both `single`/`batch` processing mode to not overwrite
files accidentally in `single` mode (#18)
* Removed `block_size` parameter from `FilteredSpectrogram` (#22)
* Sample rate is always integer (#23)
* Converted all docstrings to the numpydoc format (#48)
* Batch processing continues if non-audio files are given (#53)
* Added code quality checks (#61)
* Added coverage measuring (#74)
* Added `--down`` option to evaluate only downbeats (#76)
* Removed option to normalise the observations (#95)
* Moved filterbank related argument parser to `FilterbankProcessor` (#96)

Version 0.12.1 (release date: 2016-01-22)
-----------------------------------------

Added Python 3 compatibility to setup.py (needed for the tutorials to work)

Version 0.12 (release date: 2015-10-16)
---------------------------------------

Initial public release of madmom

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

madmom-0.14.tar.gz (16.3 MB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page