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https://github.com/CPJKU/madmom

Python audio and music signal processing library
https://github.com/CPJKU/madmom

audio-analysis cython machine-learning music-information-retrieval numpy python scipy signal-processing

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Python audio and music signal processing library

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README 

        
======

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 `_ file.



Model and data files

--------------------



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 `_.



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

3.5 or newer and the following packages installed:



- `numpy `_

- `scipy `_

- `cython `_

- `mido `_



In order to test your installation, process live audio input, or have improved

FFT performance, additionally install these packages:



- `pytest `_

- `pyaudio `_

- `pyfftw `_



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!



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) `_::



    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 pytest



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 `_

  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 <../../../madmom_models>`_

  pre-trained model/data files (see the License section)

`/madmom/utils `_

  misc stuff (e.g. MIDI and general file handling)

`/tests `_

  tests



Executable programs

-------------------



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.



Additional resources

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



Mailing list

------------



The `mailing list `_ 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



Citation

========



If you use madmom in your work, please consider citing it:



.. code-block:: latex



   @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}

   }



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*,

    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.



Acknowledgements

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



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.