As of two weeks ago, you can get a couple of hours for unlimited (rate-limited) testing, just go to top up your account (or try to search) and click the PoW local and private captcha to get that.
Even just in my family, the attitude has shifted significantly over the last year. Most of my family members are now critical of it and its effects.
Add to this that if ~6B people are using the internet (https://en.wikipedia.org/wiki/Global_Internet_usage), and ChatGPT only has almost a billion users (and is the largest player in the space), then I’d argue that LLM-users are in fact the minority.
I think this is the optimal outcome of the “Slop Police.” Normalization of these acknowledgements. Transparency is good, like a journalist declaring whether they have vested interests.
imho part of the communication problem is that a 6 year reduction actually is large, but it doesn't sound large. Smoking takes about 10 years off your life, and it's deadly in a very mechanical and understood way.
Might be the only thing that matters to you. And, perhaps, the only thing that matters in a functional sense. But, whether it’s human-coded/written or not matters deeply to some.
The code mentions "autocorrelation" method: this is a method where you multiply the signal with delayed version of itself: result = sum(x[i] * x[i - delay] for i in some range). You vary the "delay" and pick the value that maximizes the result. This is based on the idea, that the sequential periods of the signal should be similar to each other.
Not a very good method, prone to octave errors (showing pitch one octave lower than the correct one). Furthermore, the "delay" is an integer which limits the precision, so you need to use some form of interpolation. Also it doesn't allow to recognize multiple notes sounding together. Also, slow.
You can read the paper on the "YIN" pitch estimation algorithm which describes the method in details.
I think FFT-based methods are more reliable. I did little experimentation and when measuring a pure sine wave, the frequency can be determined with high precision (tenths-hundredths of a Herz). Not so good in presence of a noise or multiple instruments - I tried to use descending from the hill optimization to figure out the pitch of each harmonic, but it didn't work out.
I implemented the McLeod NSDF pitch method, which normalizes the autocorrelation to get a pretty reliable estimate with fewer octave jumps. For precise tuning I used phase tracking between successive single-bin DFTs tuned to the target frequency.
Ah, that does look like something I can work with - thanks for the legwork, I will check it out and see if its worthwhile converting to C/C++ for my device ..
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