PYIN: A fundamental frequency estimator using probabilistic threshold distributions

By Matthias Mauch, Simon E. Dixon. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2014

455 citations recorded by the source corpus.

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NANSY++: Unified Voice Synthesis with Neural Analysis and Synthesis (2022, methodology): “For the baseline models, we chose 4 popular F 0 estimators, that is, praat, rapt, pyin, and crepe (Boersma et al., 1993; Boersma & Van Heuven, 2001; Jadoul et al., 2018; Talkin & Kleijn, 1995; Mauch & Dixon, 2014; Kim et al., 2018).”
MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research (2014, methodology): “The monophonic pitch tracking algorithm used was pYIN [11] which is an improved, probabilistic version of the well-known YIN algorithm.”
Scaling Speech Technology to 1, 000+ Languages (2023, methodology): “The heuristic computes the variance of the pitch on voiced frames and removes utterances with high variance in the pitch; the pitch estimation is based on Mauch and Dixon [2014].”
Crepe: A Convolutional Representation for Pitch Estimation (2018, methodology): “More recent approaches include SWIPE [12], which performs template matching with the spectrum of a sawtooth waveform, and pYIN [13], a probabilistic variant of YIN that uses a Hidden Markov Model (HMM) to decode the most probable sequence of pitch values.”