Enhanced autocorrelation

eac.py

"""Perform enhanced autocorrelation on a wave file.
Based very loosely on https://bitbucket.org/yeisoneng/python-eac
which is based on Audacity's implementation. This version uses
Numpy features to significantly speed up processing."""
from __future__ import division
import numpy as np
from numpy.fft.fftpack import fft, rfft
from scipy.interpolate import interp1d
from scipy.signal      import argrelextrema

def eac(sig, winsize=512, rate=44100):
	"""Return the dominant frequency in a signal."""
	s = np.reshape(sig[:len(sig)//winsize*winsize], (-1, winsize))
	s = np.multiply(s, np.hanning(winsize))
	f = fft(s)
	p = (f.real**2 + f.imag**2)**(1/3)
	f = rfft(p).real
	q = f.sum(0)/s.shape[1]
	q[q < 0] = 0
	intpf = interp1d(np.arange(winsize//2), q[:winsize//2])
	intp = intpf(np.linspace(0, winsize//2-1, winsize))
	qs = q[:winsize//2] - intp[:winsize//2]
	qs[qs < 0] = 0
	return rate/qs.argmax()