Autocorrelation is a mathematical technique used to measure the similarity between a signal and a time-shifted version of itself. In the context of pitch detection, autocorrelation is used to find the fundamental frequency of a sound by identifying the repeating patterns in the signal.
Autocorrelation can be used on any type of signal that contains harmonic content, such as musical instruments, human speech, or synthetic sounds. It is particularly effective for periodic signals, which have a repeating pattern.
Unlike other methods of pitch detection, autocorrelation does not require any prior knowledge about the signal or the fundamental frequency. It is a simple and efficient method that can be used on a wide range of signals.
No, autocorrelation is primarily used for monophonic pitch detection, meaning it can only detect one pitch at a time. Polyphonic signals, which contain multiple pitches, require more complex methods of pitch detection.
One limitation of autocorrelation is that it is sensitive to noise and other disturbances in the signal, which can affect the accuracy of the results. Additionally, it may not work well for signals with a low signal-to-noise ratio or for signals with a complex harmonic structure.