Nice. To the folks saying "nothing to see here" this appears to be a variation of filter-bank spectral analysis where each band varies in frequency to track "the" in-band sinusoid. Somewhat like a bank of PLLs each with its own tracking bandpass filter. By using IIR filters rather than FFTs you avoid the latency of buffering up a full frame of data before you can run the FFT analysis. I am curious how this handles input containing broadband transients. It might be interesting to use CIC filters rather than an IIR lowpass to get better time selectivity, but maybe that's already been addressed, I didn't read the papers.
I don’t like the bins drifting from so far away so slowly. There needs to be repulsive force that prevents the bins from colliding.
It would be interesting if the resonators could adaptively model timbre to factor out harmonics while still handling unique timbre at each frequency. That could produce a pitch diagram color coded by instrument.
Edit: I bet you could fork a resonator and run over the window it just finished in reverse to correct the drift.
A useful demo for this kind of tool would be a side-by-side on difficult inputs: fast pitch bends, dense chords, and low-SNR recordings. Latency is easy to appreciate visually, but robustness under messy audio is what usually decides whether spectral tools become part of a workflow. Even a small set of repeatable test clips would make the tradeoff much clearer.
There are some piano tuners I've found who are a bit on the spectrum, who believe they can tune a piano in a way that no digital device can replicate. I'm skeptical, and would like to see how this method holds up against one of these savants.
Spectral analysis has indeed been around as a concept for centuries and there have been apps based on the FFT for decades, so definitely nothing new there.
What I have implemented however, while based in known concepts and techniques, allows to achieve real-time, low latency and high resolution (both in time and frequency dimensions) performance that I believe are out of reach of established (published) methods.
The apps you link are most likely making use of the FFT, which has become widely supported with efficient hardware acceleration and easy to use libraries, because of its central role in ubiquitous DSP applications, e.g. compression.
I would be interested in any publications or at least technical descriptions of algorithms/systems that achieve similar performance!
It is more complex than the one described here. The idea is the same but for a working solution many different coefficients are needed and adjusted properly. Resonances are adjusted to have some match to the human perception.
It is all time domain as there are no real frequencies in sound.
It is good to see the idea investigated by more people but the man should not try to claim it as his own. We are doing such tings for years and I want this knowledge stays to people so no one should claim it
Sounds really interesting! Could you share some description of the algorithm used for chord detection? What model of tonality are you using for pitch/chord naming?
It would be interesting if the resonators could adaptively model timbre to factor out harmonics while still handling unique timbre at each frequency. That could produce a pitch diagram color coded by instrument.
Edit: I bet you could fork a resonator and run over the window it just finished in reverse to correct the drift.
Also very old stuff :)
https://apps.apple.com/us/app/chord-detector/id1495811175
It is good to see the idea investigated by more people but the man should not try to claim it as his own. We are doing such tings for years and I want this knowledge stays to people so no one should claim it