NAME

       spectral analysis - perform realtime spectral analysis

SYNOPSIS

       numm-run FILE

DESCRIPTION

       Frequency  makes  for  a meaningful description of many audio signals.  We can use numpy's
       fourier analysis to compute spectra from the microphone and display the results  visually.
       We will break down the process into smaller parts: baby steps...

       First, create and save a skeletal file that moves a line across the screen:

              idx = 0
              def video_out(a):
                  global idx
                  a[:,idx] = 255
                  idx = (idx + 1) % a.shape[1]
              def audio_in(a):
                  pass

       Save this snippet and run it with numm-run.

       We  will  use  the numpy.fft module for our analysis.  First we define a function to get a
       particular frequency from the fourier transform:

              import numpy as np
              def get_freq(fourier, frequency):
                  freqs = np.fft.fftfreq(len(fourier), 1/44100.0)
                  nearest = (abs(freqs - frequency)).argmin()
                  return abs(fourier[nearest])

       Next, we hook up this function to audio input from the microphone.   A  frequency  bin  is
       chosen on a log scale for each row on the screen to display a spectogram.  In total:

              import numpy as np
              idx = 0
              recent_audio = np.zeros(4096, np.int16)
              recent_video = np.zeros((240,320,3), np.uint8)
              freq_bins = np.exp2(np.linspace(np.log2(27000),np.log2(27),240))

              def get_freq(fourier, frequency):
                  freqs = np.fft.fftfreq(len(fourier), 1/44100.0)
                  nearest = (abs(freqs - frequency)).argmin()
                  return abs(fourier[nearest])

              def video_out(a):
                  global idx
                  fourier=np.fft.fft(recent_audio)
                  values =np.array([get_freq(fourier,X) for X in freq_bins])
                  recent_video[:,idx,1] = (values/10000).clip(0,255)
                  idx = (idx + 1) % a.shape[1]
                  a[:] = np.roll(recent_video, -idx, axis=1)

              def audio_in(a):
                  recent_audio[:] = np.roll(recent_audio, len(a))
                  recent_audio[:len(a)] = a.mean(axis=1)

SEE ALSO

       numm-run(1), numm.getting-started(7), numm.one-bit-instrument(7)