numpy.correlate and autocorrelation; audio signal I want to calculate delay between an input and an output audio signal of my audio processing system. The input and output signals are available as signed 16 bit integers. To try out, I tried the autocorrelation of the input signal with the following numpy commands:

import numpy as np
import wave
wfp = wave.open('test.wav', 'rb')
samples = wfp.readframes(wfp.getnframes())
signal = np.frombuffer(samples, np.int16)
corr = np.correlate(signal, signal, "full")


I assumed that the peak of the autocorrelation is always at lag 0, which is at the index corr.size / 2 in the corr array. However I get different values for the index, when I calculate np.argmax(corr).

When I normalize the signal first to get values between -1.0 and +1.0, the peak is always at corr.size / 2 as expected, at least in my tests with different signals.

For normalization, I used the following steps:

signal = signal / float(0xFFFF)


Can someone please explain to me, why I have to normalize the signal.
Having had the same problem and without success to find a tool to sync the start of video/audio recordings automatically,
I decided to make syncstart (github).
The basic code is this:
import numpy as np
from scipy import fft
from scipy.io import wavfile
r1,s1 = wavfile.read(in1)
r2,s2 = wavfile.read(in2)
assert r1==r2, &quot;syncstart normalizes using ffmpeg&quot;
fs = r1
ls1 = len(s1)
ls2 = len(s2)
padsize = ls1+ls2+1
padsize = 2**(int(np.log(padsize)/np.log(2))+1)
s1pad = np.zeros(padsize)
s1pad[:ls1] = s1
s2pad = np.zeros(padsize)
s2pad[:ls2] = s2
corr = fft.ifft(fft.fft(s1pad)*np.conj(fft.fft(s2pad)))
ca = np.absolute(corr)
xmax = np.argmax(ca)
if xmax &gt; padsize // 2:
    file,offset = in2,(padsize-xmax)/fs
else:
    file,offset = in1,xmax/fs