Name
Abstract | ||
|---|---|---|
We propose the Probabilistic YIN (PYIN) algorithm, a modification of the well-known YIN algorithm for fundamental frequency (F0) estimation. Conventional YIN is a simple yet effective method for frame-wise monophonic F0 estimation and remains one of the most popular methods in this domain. In order to eliminate short-term errors, outputs of frequency 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 estimate per frame. To address this problem we modify YIN to output multiple pitch candidates with associated probabilities (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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6853678 | ICASSP |
Keywords | Field | DocType |
statistical distributions,speech processing,fundamental frequency estimators,probabilistic yin algorithm,pyin algorithm,viterbi,pitch tracking,frequency estimation,open source c++ library,probabilistic threshold distributions,fundamental frequency estimation,viterbi decoding,pitch estimation,computational complexity,post-processing,yin,yin threshold parameter,framewise monophonic f0 estimation,pitch track,hidden markov models,short-term errors,hidden markov model,vamp hosts,post processing,databases,algorithm design and analysis,probabilistic logic | Fundamental frequency,Pattern recognition,Computer science,Effective method,Precision and recall,Artificial intelligence,Probabilistic logic,Hidden Markov model,Prior probability,Estimator,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
1520-6149 | 46 | 2.14 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Mauch | 1 | 381 | 26.97 |
| Simon Dixon | 2 | 1164 | 107.57 |