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Luis Figueira, 2012-10-03 02:43 PM


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?
    • Slide: how our estimator will work
  • 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?
  • Create signal_processing.py and test_signal_processing.py
    • Slide: how our estimator will work
  • ipython: write rms function
    import numpy as np 
    
    def rms(array):
        return np.sqrt(np.mean(samples**2))
    
    
  • 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?
      np.ceil(samples/512)
      • don't forget to mention tghat ceil returns a float
    • what are the contents of the first frame?
      samples[0:512]
    • what are the contents of the second frame?
      samples[512:1024]
    • 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