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use full decimation instead of fast #133

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use full decimation instead of fast #133

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9 changes: 3 additions & 6 deletions friture/audioproc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,9 +43,6 @@ def __init__(self):
def analyzelive(self, samples):
samples = self.decimate(samples)

# uncomment the following to disable the decimation altogether
# decimation = 1
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I tried setting self.decimation = 1 here, and that just resulted an error on the next line, "Operands cannot be broadcast together with shapes (4096,) (256,)"; so I think this comment is outdated

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@egitto egitto Jan 23, 2020 •
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(I also tried using self.decimation = 1 and "fixing" the below line to make the shapes line up, but that just resulted in this error instead; I guess the value of self.decimation affects a bunch of places)
image

fft = rfft(samples * repeat(self.window, len(samples)//len(self.window)))

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oh, lol, actually settings self.decimation = 1 works just fine, you just have to do it in the set_maxfreq function so that it never sets it to any non-1 value
image


# FFT for a linear transformation in frequency scale
fft = rfft(samples * self.window)
spectrum = self.norm_square(fft)
Expand All @@ -60,9 +57,9 @@ def decimate(self, samples):
if self.decimation > 1:
samples.shape = len(samples) // self.decimation, self.decimation
# the full way
# samples = samples.mean(axis=1)
# the simplest way
samples = samples[:, 0]
samples = samples.mean(axis=1)
# the fastest way
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(I didn't actually profile this, idk how much computation time goes here vs actually doing the fft and rendering)

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hm, I just profiled (with a value of self.decimation == 16) and with the faster way, the decimation function takes about 7.05% of a the FFT's duration, and with the slower way it takes about 93.1% of the FFT's duration. So, a pretty significant cost, assuming the FFT is the expensivest part of this process.

# samples = samples[:, 0]
return samples

def set_fftsize(self, fft_size):
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