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?
*** *Slide*: how our estimator will work
* 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?
** Create signal_processing.py and test_signal_processing.py
*** *Slide*: how our estimator will work
** ipython: write rms function
<pre>
import numpy as np

def rms(array):
return np.sqrt(np.mean(samples**2))

</pre>
** write RMS tests
** paste RMS function to signal_processing.py
** nosetests signal_processing.py
* in ipython again
** how many frames (non overlapping) does the signal have?
<pre>np.ceil(samples/512)</pre>
*** don't forget to mention tghat ceil returns a float
** what are the contents of the first frame?
<pre>samples[0:512]</pre>
** what are the contents of the second frame?
<pre>samples[512:1024]</pre>
** Generalising....
* signal_processing.py
** def get_frame_count(n_samples, increment)
** def get_frame(samples, increment, n)
** write tests for both functions
** ipython: protoype both functions, write them, test them
* implement detection function on tempo_estimator.py
* implement tests
* Autocorrelation+ testing
* plot waveform and detection function
** time scales are wrong
* create plots.py just as a simple plotting script
* in tempo_estimator.py create bpm_to_lag and lag_to_bpm functions
* add to tempo estimator definition bpm_min and bpm_max