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I'm unable to compute $f_0$ (fundamental frequency) in librosa feature extraction. By ready much in github issues check the second comment. I see that $f_0$ is correlated with some other features (like zero_crosssing) easy to extract from librosa.

What are these features except for that one I give? Is this correlation super high so that they can replace $f_0$?

data sample

site for data

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  • $\begingroup$ I mean fundamental frequency, I edited the post. $\endgroup$ – abdoulsn Nov 17 '19 at 18:11
  • $\begingroup$ updated again the post. $\endgroup$ – abdoulsn Nov 17 '19 at 18:18
  • $\begingroup$ so, are you specifically interested in a YIN-based fundamental frequency estimate, or is the question more generally, "what features correlate well with fundamental frequency"? In either case, are we looking at a specific kind of signal (speech, instrumental music, birdsong, cars...)? $\endgroup$ – Marcus Müller Nov 17 '19 at 18:29
  • $\begingroup$ This is generally question, I'm using speech emotion data(.wav audio file with one expression). $\endgroup$ – abdoulsn Nov 17 '19 at 18:30
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    $\begingroup$ Like, upload one of your .wav files somewhere and link to that! $\endgroup$ – Marcus Müller Nov 17 '19 at 18:35
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Here is the answer as responded here in github.

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