pYIN: a fundamental frequency estimator using probabilistic threshold distributions

We propose the Probabilistic YIN (PYIN) algorithm, a mod-ification of the well-known YIN algorithm for fundamental frequency (F0) estimation. Conventional YIN is a simple yet effective method for frame-wise monophonic F0 estima-tion and remains one of the most popular methods in this do-main. In order to eliminate short-term errors, outputs of fre-quency estimators are usually post-processed resulting in a smoother pitch track. One shortcoming of YIN is that such post-processing cannot fall back on alternative interpretations of the signal because the method outputs precisely one es-timate per frame. To address this problem we modify YIN to output multiple pitch candidates with associated probabili-ties (PYIN Stage 1). These probabilities arise naturally from a prior distribution on the YIN threshold parameter. We use these probabilities as observations in a hidden Markov model, which is Viterbi-decoded to produce an improved pitch track (PYIN Stage 2). We demonstrate that the combination of Stages 1 and 2 raises recall and precision substantially. The additional computational complexity of PYIN over YIN is low. We make the method freely available online1 as an open source C++ library for Vamp hosts. Index Terms — Pitch estimation, pitch tracking, YIN 1