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Two algorithms to detect the fundamental frequency of a signal: one in the time domain (Autocorrelation) and one in the frequency domain (Harmonic Product Spectrum / HPS)

Autocorrelation algorithm


Fundamentally, this algorithm exploits the fact that a periodic signal, even if it is not a pure sine wave, will be similar from one period to the next. This is true even if the amplitude of the signal is changing in time, provided those changes do not occur too quickly.

To detect the pitch, we take a window of the signal, with a length at least twice as long as the longest period that we might detect. In our case, this corresponded to a length of 1200 samples, given a sampling rate of 44,100 KHz.

Using this section of signal, we generate the autocorrelation function r(s) defined as the sum of the pointwise absolute difference between the two signals over some interval, perhaps 600 points.

Graphically, this corresponds to the following:

Shifting the signal

Here, the blue signal is the original and the green signal is a copy of the original, shifted left by an amount nearing the fundamental period. Notice how the signals begin to align with each other as the shift amount nears the fundamental period.

Intuitively, it should make sense that as the shift value s begins to reach the fundamental period of the signal T, the difference between the shifted signal and the original signal will begin to decrease. Indeed, we can see this in the plot below, in which the autocorrelation function rapidly approaches zero at the fundamental period.

Autocorrelation function

The fundamental period is indentified as the first minimum of the autocorrelation function. Notice that the function is periodic, as we expect. r(s) measured the total difference between the signal and its shifted copy, so the shifs approach k*T, the signals again align and the difference approaches zero.

We can detect this value by differentiating the autocorrelation function and then looking for a change of sign, which yields critical points. We then look at the direction of the sign change across points (positive difference to negative), to take only the minima. We then search for the first minimum below some threshold, i.e. the minimum corresponding to the smallest s. The location of this minimum gives us the fundamental period of the windowed portion of signal, from which we can easily determine the frequency using


Clearly, this algorithm requires a great deal of computation. First, we generate the autocorrelation function r(s) for some positive range of s. For each value of s, we need to compute the total difference between the shifted signals. Next, we need to differentiate this signal and search for the minimum, finally determining the correct minimum. We must do this for each window.

In generating the r(s) function, we define a domain for s of 0 to 599. This allows for fundamental frequencies between about 50 and 22000 Hz, which works nicely for human voice. However, this does require calculating r(s) 600 times for each window.

Questions & Answers

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At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
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Source:  OpenStax, Ece 301 projects fall 2003. OpenStax CNX. Jan 22, 2004 Download for free at http://cnx.org/content/col10223/1.5
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