AHA! I've got the list of Paper 1 speakers. Journalist skills coming in handy.
I'm starting to wonder whether we should have two models: the original Olive model, for those who only trust the original double-blind peer-reviewed paper, don't like wild guesses, and/or are comparing speakers that are known to be similar to those in the Olive test (i.e. non-coaxial monopoles). And a separate "experimental" model that could be built from scratch based on what we know about perception of spinoramas (e.g. the importance of the DI curve, the relative unimportance of overall tilt), that might make more sense to use for non-standard speakers and would come with a fat warning that it is not directly backed by double-blind testing data and should therefore be taken with a huge grain of salt. The experimental model could be calibrated against the Olive model by aligning the scores for "standard" speakers. But even then, if we are asked to quantify how accurate that experimental model would be, our best answer would be ¯\_(ツ)_/¯
I think this is a great idea.
However, purporting to give an experimental "preference" model would be overstepping IMHO, given that we couldn't possibly derive it from valid preference data.
I would suggest instead that we simply give speakers a rating for each of:
Optionally, additional sub-ratings could be given for HER and VER.
- ON deviation from flat
- LFX
- PIR (or ER) deviation from line of best fit (defined mathematically such that it is independent of slope, in contrast to the Olive paper).
A nonlinear distortion rating could also be added, although IMHO this would not be possible on the basis of the limited distortion measurements Amir currently performs.
Finally, if we wanted to be a bit more experimental about it (which I think we should be), we could apply equal-loudness based weightings to each metric.
In other words, a deviation from flat in the ON at say 15kHz would be penalised less harshly than a deviation at say 3kHz. And so forth...
This weighting could be derived from existing data from psychoacoustic research.
I was thinking about experimenting with seeing how just using the % weighting for each parameter would look like compared to the formula weighting. The score range may not be the same, but I would assume the ranking would stay the same; if so, I could then experiment with normalizing the PIR and running the NBD score just on that, and having that take the combined % that the original NBD_PIR & SM_PIR take up.
The formula is this:Normalising the PIR and running the NBD score on it sounds like a very elegant solution to me
I'm not sure I understood the first part about using the % weighting for each parameter as opposed to the formula weighting?
The formula is this:
View attachment 65702
the weighting is this:
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You can’t just multiply the scores by these weights though (unless it’s a perfect score), as the numerical value wouldn‘t match, but I would assume the ranking would stay the same.
To give an example, you can curve a set of data, the numerical values will change, but the order won’t.
The best score (to get a 10) for LFX isn’t a 1.0, that’s why.You know, this is something I'd never understood in the paper. If you add up the 4 coefficients in the formula, you get 12.11, not 12.69. I can't work out why this would be the case?
Anyway, if it were up to me, I wouldn't be trying to tweak the Olive model. I think it is what it is, and without the raw data, we can't know whether tweaks made to it would increase or decrease correlation with listener preference.
Instead, I'd simply build a new model that doesn't purport to be a preference model, basically along the lines I suggested in post #407.
I've attached 24 ppo
I think this is a great idea.
However, purporting to give an experimental "preference" model would be overstepping IMHO, given that we couldn't possibly derive it from valid preference data.
I would suggest instead that we simply give speakers a rating for each of:
- ON deviation from flat
- LFX
- PIR (or ER) deviation from line of best fit (defined mathematically such that it is independent of slope, in contrast to the Olive paper).
IMHO there are 2 paremeters with PIR which are important: one is average (squarreed?) deviation from best fit line and the other is slope of the best fit line, as if slope is too small or too high it would impact the overall tonal balance.
P.S. What is "ON"?
I agree. However, it's not established that any particular slope is superior (or likely to be most preferred). This is illustrated by the two Olive studies, where preferred slope proved to be dependent on the average slope of each sample.
Deviation, OTOH, is better established to correlate with listener preference.
Therefore, I think that deviation, but not slope, should be a rated parameter.
On-axis. I'm just borrowing the terminology from the Olive paper.
Keep in mind that EQing to obtain a slope is not the same as inherent slope, which is achieved based on the directivity of the loudspeaker, and of which no real consensus has been reached as to what is ideal.If we can agree that PIR with slope of 5 deg would intuitively sound "bright" and the one with slope of 45 deg would sound "dark" we can also agree that indicates there is an optimally sounding region of slopes somewhere in between.
For example, that is how I adjusted PIR of Sony speaker, I didn't only make it smoother but I also increased a slope a little to avoid too bright tonal balance.
Aha. In that case my vote would instead go in favor of using LW measured within +/-15 deg or similar.
Yeah, that WebPlotDigitizer is a great tool to use. Pretty simple too.
Keep in mind that EQing to obtain a slope is not the same as inherent slope, which is achieved based on the directivity of the loudspeaker, and of which no real consensus has been reached as to what is ideal.