Ismir2012: Software Session Plan¶

Length and plan¶

• 90 min (75 + 15m interval)

Software Requirements¶

• Python
• Numpy
• Matplotlib
• Nose and Nosetests
• ipython (desirable, not mandatory)

Repository¶

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

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)
  

  import tempo_estimator as est
  import sys
  
  filename = sys.argv[1]
  
  tempo = est.estimate_tempo_of_file(filename)
  

• Easy Mercurial:
  • ignore pyc and py~
  • commit estimate_tempo.py
  • commit .hgignore
• Create estimate_tempo.py
• Create test file test_estimate_tempo.py
  

  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 
  
  

• Commit both files
• Start writing the code of tempo_estimator.py
  

• Slide: how to detect tempo?
• Write the onset detection function and autocorrelation function calls in tempo_estimator.py
  

• Commit
• Python Shell:
  • import the audiofile module
  • load the audiofile
    

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

• split the file into non overlapping blocks and calculate the RMS of each
• Hot to calculate the RMS?
  

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

• test rms function working
• paste it to tempo_estimator.py and build a couple of tests