systematic comparison of methods about pitch-detection HOT 6 OPEN

btw, I could provide python code for evaluating pyin (taken from librosa) on the NSynth-test split, if you are interested. It requires to manually download the data to a local directory though, but that should not be a big issue.

from pitch-detection.

By my method, do you mean PMPM/Probablistic McLeod Pitch Method?

from pitch-detection.

yes, as you mention it in the README

from pitch-detection.

It's a good idea. I think some scripts around the fetching, loading, running pitch tracking, and evaluating the quality of the results on the NSynth data would be a good addition to the repo (and maybe even incorporating modern pitch tracking methods - CREPE is the end of my memory but there are a lot of new ones (like PESTO https://zenodo.org/records/10265343)

from pitch-detection.

I think CREPE is still one of the best estimators. There are some recent (SSL) works on general music representations that perform better in some cases when fine-tuned for pitch e.g. MERT, but they also have disadvantages (based on Transformer is likely to result in higher latency for real-time applications)
See also this benchmark where NSynth is one of the tasks

from pitch-detection.

As long as it's a configurable or well-known path e.g. ~/NSYNTH_DATA, that makes sense to me.

from pitch-detection.