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Iso 27000

05-22-2009

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Iso 27000

ISO 27000:2009, the overview document for the 27000 family of standards, is now published and available as a free download. It outlines the 27000 standards (to date) and provides a very brief glossary. For some reason the standard comes as a zip archive file of a PDF. When you go to the link, you will be briefly redirected to a licence page, and have to agree in order to get the document.

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LEARN ABOUT DEBIAN

numm.spectral-analysis

spectral-analysis(7)						  Numm Tutorials					      spectral-analysis(7)

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 micro-
       phone 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)

numm								   February 2012					      spectral-analysis(7)