Metadata-Version: 2.4
Name: music
Version: 0.1.dev4
Summary: MASS is Music and Audio in Sample Sequences
Home-page: https://github.com/ttm/mass
Author: Renato Fabbri
Author-email: listamacambira@googlegroups.com
License: MIT
Keywords: music audio dsp psychophysics physics synthesis toolbox
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Telecommunications Industry
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Religion
Classifier: Intended Audience :: Other Audience
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Multimedia :: Sound/Audio :: Sound Synthesis
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Topic :: Multimedia :: Sound/Audio :: Editors
Classifier: Topic :: Multimedia :: Sound/Audio :: Mixers
Classifier: Topic :: Multimedia :: Sound/Audio :: Speech
Classifier: Topic :: Artistic Software
Classifier: Topic :: Other/Nonlisted Topic
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
License-File: LICENSE
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: summary

==================================================================
MASS is Music and Audio in Sample Sequences
==================================================================

This project delivers routines for music oriented sound synthesis
in a sample based system. MASS can be though of as a sample level
DAW system, in which the objects manipulated are in fact the array
of samples describing the sound wave that will reach a listener ear.

All parameters are updated in a sample by sample rate and calculations
are made in 64 bit floating point. All operations are described by
equations that directly relates audio samples to musical aspects.
Detailed descriptions can be found in:

- the article "Psychophysics of musical elements in the discrete-time representation of sound": http://arxiv.org/abs/1412.6853

- the msc dissertation "Música no áudio digital: descrição psicofísica e caixa de ferramentas": https://github.com/ttm/dissertacao/blob/master/dissertacaoCorrigida.pdf?raw=true

Usage example
=================
Download messages from one GMANE list:

.. code:: python

    import mass as m

    # sine, triangle, square and sawtooth
    # are attributes of the bt object:
    bt=m.BasicTables()

    # for frequency to midi and decibels to amplitude relations:
    co=m.BasicConverter()
    # try co.db2Amp .amp2Db .hz2Midi .midi2Hz 

    sy=m.Synth()

    note=sy.render()
    ut=m.Utils()
    ut.write(note) # saved to fooname.wav

    melody=n.hstack([sy.render(f,.2) for f in 
                      co.p2f(220,[0,7,7,5,6,7,0,4,7,0])])
    sy.vib_depth=3.
    sy.vib_freq=3.
    sy.tab=bt.saw
    section2=n.hstack([sy.render(freq,dur) for freq,dur in 
                        zip(co.p2f(110,[0,7,7,5,6,7,0,4,7,0]),
                        [.2,.4,.2,.2,.8,.2,.4,.2,.4])])

    song=n.hstack((melody,section2))

    ut.write(song,"song.wav")


    # more in the way. Take a look at the above literature.
    # Enjoy!
