NumPy Question: Audio Frequency Spectrum / Signal Analysis - pe_audioLevels.py [0/1]

[Perry Kivolowitz]

[This followup was posted to comp.lang.python and a copy was sent to the cited author.]

I'm having a little touble writing a graphical view of an audio 
spectrum. I'm using NumPy, and Useful Things - a Python programmable 
plug-in for Adobe After Effects.

I am totally inexperienced with respect to signal processing, so please 
excuse my (possibly) bogus use of terminology.

It seems to work, with the exception that sounds close to silence 
(highly negative DB) are putting out enormous amplitudes. So much so, I 
have to put in a base DB check - squelching anything more negative to 0.

This is the code which converts from samples to frequency data:

	m = 2.0 / float(numSamples)
	halfSamples = numSamples / 2
	samplePeriod = 1.0 / float(sampleRate) * numSamples
	firstHarmonic = 1.0 / samplePeriod
	fftData = fft(samples)
	realTerms = fftData[1 : halfSamples + 1]
then I do:

	frequencyAmplitudes = [ 0 ] * halfSamples
	for i in xrange(halfSamples):
	 frequencyAmplitudes[i] = convertToDecibels(m * abs(realTerms[i]))

to gather up data from a specific band I finish with (to show the min DB 
test):

	outputData[i] = 1.0 - (abs(bandAccumulator / bandCounter) - minDB) 
/ (maxDB - minDB)
	if outputData[i] > 1: outputData[i] = 1
	if outputData[i] < 0: outputData[i] = 0

where convertToDecibels is:

amp2db = 20.0 / log(10.0)

def convertToDecibels(a):
	if a < 1.0e-30: return -600
	else: return amp2db * log(a)

When the min DB setting is played with, I get great results. Without it 
being set right, I get blown out in the lower frequencies. 

If this isn't enough, I have enclosed the entire source. If you're an 
After Effects user, I can supply a free license to Useful Things so you 
can actually see what's going on (and code your own After Effects plug-
ins in Python!) in exchange for your help.

Thanks