> Bruce Wiggins's (I hope) research was what started this fray out in the first >place Yup. And several others. But the point is that there is a good deal more to be done, especially as you point out that:
> this sort of optimization retains the blackbox leanings of machine learning > as >a general discipline Which would be OK, if the black box could give a meaningful rating of which decoders are good and which are bad, or more to the point, which is better than another. But we're not to that point yet. > how many actually take a look at the early bispectral model of Gerzon? Took a look at. But that's not the same thing as implementing it! On the side of improving the psychoacoustic models I've been working on using spherical head models to predict the localization cues achieved and making in situ measurements of the ear signals of a real listener when listening to Ambisonic reproduction. Some of this is available in: "Why Ambisonics Does Work ", Benjamin, Lee and Heller, AES preprint 8242 (2010) a paper which was semi-humorous but which also contains some good stuff. I was partly unsuccessful at showing the relationship between Gerzon's Energy vector and ILDs and that is something which I will devote some further serious attention to soon. > a more well-thought out optimization criterion, with some intelligent, >psychoacoustically minded regularization built in <snip> could still cut the >mustard Ah, if only we could find some intelligence to apply to the problem. Eric ----- Original Message ---- From: Sampo Syreeni <de...@iki.fi> To: Surround Sound discussion group <sursound@music.vt.edu> Sent: Wed, February 29, 2012 2:19:46 PM Subject: Re: [Sursound] Decoding coefficients for non symmetrical setups On 2012-02-29, Gregory Maxwell wrote: >> Would an automated “blind" search algorithm possibly > > Speaking of that, you probably want to search the list archives for a thread > I >started in 2009 titled: > > "A stupid optimizer for irregular ambisonic layouts" > > In it I provide the source for a simplistic decoder that uses a generic open >source blackbox non-linear optimizer library with a simple objective to make >matrixes. Before They point it out themselves, I think the fourth installment of Blah does very much the same. And of course Bruce Wiggins's (I hope) research was what started this fray out in the first place. So, yes, this is something that seems to be recommended from more than one corner, with regard to irregular layouts. But still... Personally what I find a bit worrisome is that this sort of optimization retains the blackbox leanings of machine learning as a general discipline. None of the ambisonic specific, closed form optimization literature, or the derived specifics of the base optimization problem, are being utilized. Instead the two (sometimes simultaneous, sometimes even not that) Gerzonian equations are being fed into one or another optimization framework, with no regard to what happens then, and without feeding in all of the age-old mathematical-physical knowhow of how those systems of equations behave. Like for instance psychoacoustical sensitivity estimates from the BBC era. In addition to being a fan of black box algorithms, including all of the stuff that goes under the rubric of "data mining" (professionally I make my living as a database guy), I'm also a little bit of a skeptic towards the stuff. At least as far as the math I know and love suggests I should be. For example, when using support vector machines to fit polynomial bases, how many people actually care to evaluate the Vapnik-Cervonekis bound intrinsic to the problem, and then bound it in a principled fashion before commencing to optimize numerically? That after all is the most principled framework in which to bound overfitting by the machine -- i.e. the very same thing which leads to speaker detent within the ambisonic framework, even after simple dimensional constraints have already been dealt with. And how many actually take a look at the early bispectral model of Gerzon? Or the third one which name I don't remember right now? Even if those aren't backed up by psychoacoustics, they are still very, *very* relevant as (easily, formally, in-principled-fashion) saturable optimization criteria (in the usual ambisonic L^2 sense no less). I don't think going with the easy route and just using blackbox optimizers does the job best, here. Instead, I would think we have to find a way to inject more and more current, analytically purified, psychoacoustic knowledge into the system, before we even start to optimize. Even if numerical optimization still remains the key in reaching a local optimum in this kind of a very difficult nonlinear optimization problem. Once again, Robert Greene, please help me if I'm falling short on the hard math, somehow. > I like the generic optimization approaches _more_ than more mathematically >elegant closed form solutions because it's easy to play around with the >objective functions— and usually any change to the objective makes your closed >form solutions need to start from scratch. So to reiterate, numerical optimization is a must, because the most general problem seems to be analytically intractable. I'm even pretty sure that certain rig configurations could be shown to be impossible to solve using analytic means, and even instable around their steepest, global optimum if that was ever found. At the same time, though, I think a more well-thought out optimization criterion, with some intelligent, psychoacoustically minded regularization built in, and perhaps utilizing not only the L^2 norm but also the L^1 at the same time, could still cut the mustard. That's only going to happen if we push more and more of the post-Gerzon psychoacoustic research into the optimization criterion and then use an optimization engine capable of dealing with that sort of thing. That isn't being done now. Even to accelerate convergence, or to give a global, smooth starting point for the optimization procedure(s), or to regularize the eventual outcome. Why not? Are we really that lazy (well I am, but are the researchers in the feel as lazy as me as well?) > https://people.xiph.org/~greg/ambisonics/ambi_opt.c Under xiph.org? Ooh! Please, more of that. And then more reseach plus application in how to optimally code/decode even first order using Vorbis (or some derivative?). > Giving a brief glance at the code, now with several more years of experience >with optimization— and I see that my objective function appears >differentiable. >If I were to do this again I'd probably use a C++ reverse mode automatic >differentiation library, so that I could get a version of the objective with >gradients. Don't. With ambisonic, you will have to deal with both pantophony and periphony, and the transition between them is decidedly singular. No stock numerical library can deal with something like that, that I know of. > My email archives indicate that Aaron Heller made a version with a bunch of >improvements like RME rE optimization, and adding direction mismatch between >rE >and rV as part of the objective. Yes. That's part of the BLaH work, and very, *very* cool. But even there, the precise tradeoff between directional error in rV and rE seems to be more of an instintual decision than a one based on hard science. The resulting decoder is exceptionally good compared to anything preceding it, true, but I don't think it's necessarily the best, as a global solution, or especially that it would generalize too easily to higher orders. > Somewhere I had some version with support for higher orders and 3d but I > don't >know where that is right now. If you had it, I'd bet it'd suck -- if only a bit -- compared to the optimum 3D code we will eventually find. > There are a lot of things you can do starting from a simple framework like >this. Finally, no contest there. It's just that little nagging detail beyond which annoys me...so. :) -- Sampo Syreeni, aka decoy - de...@iki.fi, http://decoy.iki.fi/front +358-50-5756111, 025E D175 ABE5 027C 9494 EEB0 E090 8BA9 0509 85C2 _______________________________________________ Sursound mailing list Sursound@music.vt.edu https://mail.music.vt.edu/mailman/listinfo/sursound _______________________________________________ Sursound mailing list Sursound@music.vt.edu https://mail.music.vt.edu/mailman/listinfo/sursound
