ISMIR and DAFx Tutorials 2012

h1. Ismir2012: Software Session Plan

h2. Length and plan

* 90 min (75 + 15m interval)

h2. Software Requirements

* Python 

* Numpy

* Matplotlib

* Nose and Nosetests

* ipython (desirable, not mandatory)

h2. Repository

https://code.soundsoftware.ac.uk/hg/python-tutorial-skeleton

h2. Script

* Clone/Check if repository was successfully cloned

* Explore folder and files 

* Launch python shell (ipyhon)

* import audiofile.py and plot samples

* Explain what the test files are and introduction to nose 

* Create estimate_tempo.py (main programme)

<pre>

import tempo_estimator as est

import sys

filename = sys.argv[1]

tempo = est.estimate_tempo_of_file(filename)

</pre> 

* Easy Mercurial: 

** ignore pyc and py~

** commit estimate_tempo.py

** commit .hgignore

* Create estimate_tempo.py

* Create test file test_estimate_tempo.py

<pre>

import estimate_tempo as est

def test_tempo_120bpm():

    tempo = est.estimate_tempo_of_file("test files/120bpm.wav")

    # we consider the test to be passed if error is less than 1/4 of bpm

    error = abs(tempo - 120) < 0.25

    assert 

</pre>

* Commit both files

* Start writing the code of tempo_estimator.py

<pre>

</pre>

* Slide: how to detect tempo?

* Write the onset detection function and autocorrelation function calls in tempo_estimator.py

<pre></pre>

* Commit

* Python Shell: 

** import the audiofile module

** load the audiofile

<pre>

import audiofile as af

(samples, samplerate) = af.read_all_read_all_mono_samples_from_file('test files/beatbox.wav')

</pre>

** split the file into non overlapping blocks and calculate the RMS of each

** Hot to calculate the RMS?

<pre>

import numpy as np 

def rms(array):

    return sqrt(np.mean(samples**2))

</pre>

** test rms function working

** paste it to tempo_estimator.py and build a couple of tests