Computing pitch of speech and music using a sawtooth waveform inspired pitch estimator

Abstract

A powerful pitch estimation algorithm called SWIPE has been developed for processing speech and music. SWIPE is shown to outperform existing algorithms on several publicly available speech and musical instrument databases, and a disordered speech database, reducing the gross error rate by 40%, relative to the best competing algorithm. In short, SWIPE estimates the pitch as the fundamental frequency of a sawtooth waveform, whose spectrum best matches the spectrum of the input signal. The short-time Fourier transform of the sawtooth waveform provides an extension to older frequency-based, sieve-type estimation algorithms by providing smooth peaks with decaying amplitudes to correlate with the fundamental frequency (if present) and its harmonics. An improvement on the algorithm is achieved by using only the first and prime harmonics, which significantly reduces subharmonic errors commonly found in other pitch estimation algorithms.