Implementation of an Autocorrelation Pitch Detector in Application to Query by Humming

A pitch detector for application in query by humming (QBH) is implemented in this paper. This algorithm is made up of two parts: note segmentation and pitch detection. In the first part, voiced/silence decision is made on each segment of the input signal by a pattern recognition approach, and further, the preparatory note borders are obtained; then, via analysis of the instantaneous energy contour, the adjacent notes that adhere to each other are separated. In the second part, pitch is estimated for all frames contained in a note's duration by an autocorrelation method and the mean of these pitch values is taken as the average pitch of this note. Moreover, in order to remove the effect of formant structure, a nonlinear preprocessing is adopted in the pitch detection part and the autocorrelation function is properly weighted before peak picking. Finally, hummings of several experimenters with different voice characters are recorded to test this pitch detector, whose efficiency and reliability are proved by the results.