Metadata-Version: 1.1
Name: music22
Version: 0.0.2.post1
Summary: A tool for musicological analysis from audio files. For a symbolic analysis, you can use Music21 (http://web.mit.edu/music21/). Now it is focused on modal music analysis : Scale analysis, tonic detection
Home-page: https://github.com/AnasGhrab/music22
Author: Anas Ghrab
Author-email: anas.ghrab@gmail.com
License: MIT
Description: =================================================
        Music22: Modal Music Analysis
        =================================================
        
        Overview
        ========
        
        Music22 is a Python2 package for musicological analysis, especially modal music and melodies. The analysis is done from audio files.
        
        For now, it's main features are :
        
        * Fondamental frequencies extraction (using *PredominentMelody()* from **Essentia**);
        * Getting the main frequencies as peaks of the probability density function from frequencies;
        * Comparing PDFs using a correlation coefficient;
        * Getting a similarity matrix between melodies.
        
        Installation
        ============
        
        To use Music22, you need to manually install `Essentia`_. `In futur versions, it will be also possible to use` `TimeSide`_.
        
        Then, install Music22 with the following :
        
        .. code:: python
        	
        	pip install music22
        
        Or using ``git``:
        
        .. code:: python
        
        	git clone https://github.com/AnasGhrab/music22
        	python setup.py install
        
        
        .. _Essentia: http://essentia.upf.edu/
        .. _TimeSide: https://github.com/Parisson/TimeSide
        
        Basic Usage
        ===========
        
        To use Music22 :
        
        .. code:: python
        
        	from music22 import modalis,scale
        	path = "path/to/a/folder/with/audios/wav/files/"
        	Kchants = modalis.melodies(path,transpose='Yes',freqref=300)
        	
        Then you can
        
        .. code:: python
        
        	Kchants.pdf_show()
        	Kchants.matrix()
        	Kchants.melodies[0].scale
        		
        For more details, please read to the tutorial (in french) :
        
        http://nbviewer.ipython.org/github/AnasGhrab/music22/blob/master/docs/source/examples/barraq.ipynb
        
        Contact
        =======
        
        Homepage: http://anas.ghrab.tn
        
        Email:
        
         * Anas Ghrab <anas.ghrab@gmail.com>
        
        License
        =======
        
        The MIT License (MIT)
        
        Copyright (c) 2015 Anas Ghrab
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
        
Keywords: musicology analysis from non-symbolic data
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
