AutoEQ project

(2023-08-01) Global optimiser is working significantly better than the greedy optimiser. Score computations have been ported to Cython for performance
(2022-12-29) Automatic way to guess smoothness of curves
(2022-05-26) Debug target again and try to decrease preamp changes.
(2022-05-05) Corrected the rear reflections which are incorrect in the standard
(2021-09-06) Added a second optimisation algorithm that gives better results :)
(2021-04-10) Investigated how to find a target (or set of targets) that maximize the score. Use ray[tune] and random grid to find automatically optimum targets. That’s different than for a given target, find the optimal EQ.
(2021-01-30) Investigated why some speakers don’t eq well. Not sure about the answer yet but mostly:
- the target is critical and the system is not smart enough to guess it correctly each time.
- zones where the crossovers or port resonance are generating large non linearity should be ignored.
(2021-01-17) Build a script that compute all EQs :)
- Results are in text files called iir-autoeq.txt (compatible with Equalizer APO).
- The script around the algorithm has a fair number of knobs to play with.
(2021-01-13) Found a working algorithm that generates EQ as good as made by hand :)
- Greedy algorithm
- Use simmulated annealing at each steps
- Optimise for flat LW and smooth PIR
- 2 min per speaker max on 1 core
- 14 min for all eqs on all cores
- Now things are easy :)
(2021-01-09) Added local optimization with a gradient to see if I can get a better optimum.
- answer is surprising and is NO.
- I don’t understand why yet.
(2021-01-02) Optimised Listening Window with a series of PEQ. Tried various strategies:
1. Greedy algorithm: start with the highest peak and optimised for this one. Iterate.
2. Greedy algorithm: start with the highest area and optimised for this one. Iterate.
3. Optimise for L2 norm or optimised for R**2 (from linear regression on LW)
4. Same as above with a constraint on On Axis (need to stay flat)