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Before anyone yells at me I fully realize that this question has been asked numerous times. I assure you that I've read through the existing questions and answers but I am still confused about part of the problem.

I have a sound source that plays music (A) in a closed environment. I have a mic that I'm using to record A. I am left with two wav files which share the same characteristics and length (number of samples).

My goal is calculate the time it took for A to reach the mic.

I am trying to perform the calculation using cross-correlation (numpy):

# Delay estimation
corr = numpy.convolve(original_audio, recorded_audio, 'full')
delay = int(len(corr)/2) - numpy.argmax(corr)
distance = delay / sample_rate * 343 # sample_rate == 22050, m/s = speed of sound
print("Distance full: %.2f cm" % (distance * 100))

I consistently obtain values in the 300,000 cm range. The distance between the speaker and mic is approximately 2 feet.

This is all pretty new to me so I'm sure I'm missing something obvious.

Thanks in advance.

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    $\begingroup$ Are you sure you shouldn't be using numpy.correlate instead of numpy.convolve? To estimate delay, you want to cross-correlate your signals, not convolve them. You'll possibly end up with a much larger delay by convolving. $\endgroup$ – Peter K. May 8 '13 at 18:01
  • $\begingroup$ PeterK is likely correct. Note that you can implement correlation via convolution by time-reversing and conjugating one of the inputs first. This can allow you to use fast convolution algorithms (like overlap-save) for correlation. $\endgroup$ – Jason R May 8 '13 at 18:20
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Are you sure you shouldn't be using numpy.correlate instead of numpy.convolve? To estimate delay, you want to cross-correlate your signals, not convolve them. You'll possibly end up with a much larger delay by convolving.

Trying something simple:

x = [1, 0, 0, 0, 0 ];
y = [0, 0, 0, 0, 1 ];
conv = numpy.convolve(x,y); 
conv
array([0, 0, 0, 0, 1, 0, 0, 0, 0])
corr = numpy.correlate(x,y,"full");
corr
array([1, 0, 0, 0, 0, 0, 0, 0, 0])
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    $\begingroup$ This is exactly what I was looking for. Another example I had seen used convolution and it hadn't occurred to me that direct correlation would be the correct choice. Thank you. $\endgroup$ – CaymanEss May 8 '13 at 22:21

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