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Pitch detection is of interest whenever a single quasi­periodic sound source is to be studied or modeled, specifically in speech and music. Pitch detection algorithms can be divided into methods which operate in the time domain, frequency domain, or both. 

Some pitch detection methods uses the detection and timing of sometime domain feature. Other time domain methods use autocorrelation functions or difference other methods to detect similarity between the waveform and a time lagged version of waveform. Again some others use methods to operate in the frequency domain, locating sinusoidal peaks in the frequency transform of the input signal. 

Other methods use combinations of time and frequency domain techniques to detect pitch. Frequency domain methods call for the signal to be frequency transformed and then the frequency domain representation is inspected for the first harmonic, the greatest common divisor of all harmonics, or other such indications of the period. Windowing of the signal is recommended to avoid spectral smearing, and depending on the type of window, a minimum number of periods of the signal must be analyzed to enable accurate location of harmonic peaks .Various linear pre­processing steps can be used to make the process of locating frequency domain features easier, such as performing linear prediction on the signal and using the residual signal for pitch detection. Performing non­linear operations such as peak limiting also simplifies the location of harmonics. 

          Detecting the pitch of an input signal seems to be simple. Many groups have taken this challenge by simply taking the Fourier transform of the signal, and then finding the frequency with the highest spectral magnitude. As easy as it may seem, this approach does not work for many musical instruments. Instead, we have chosen to approach the problem from a more expandable point of view.


The pitch determination is very important for many speech processing algorithms. In this project, pitch detection methods via autocorrelation method, cepstrum method, and linear predictive coding (LPC) are examined.