The point is that FFTs are considered a gold standard & ubiquituous. Why was this published paper in the Journal of Audio Engineering not peer reviewed & their statement challenged based on the above? I could be completely wrong of course & am open to learning & correcting.Then they need better measuring tools no?
There are two totally different agendas that underlay Teager's work and that of my colleagues at Bell Labs. All the work that has been published in the speech area was underlain by what I call Agenda One, which is the engineering agenda. The goal there is this: we have got the speech signal outside the mouth, and we can measure it with an electret microphone, or a carbon button microphone, and then transmit that signal down a pair of wires. We can measure that signal to arbitrary accuracy. What we are after is some kind of mathematical model that we can manipulate, that will generate the signal that we measured with the microphone to arbitrary accuracy. It's totally irrelevant whether or not that model bears any resemblance to the physics of production. It only has to be a computationally efficient and adjustable model. That's it: computationally efficient and economically viable, so as to allow one to build the hardware to generate the speech signal as part of the system. That's Agenda One, and it has dominated most all of the work on speech production modeling................
On the other hand, Teager looked at that model and he said, "No, it's not right!" When he started going inside the vocal tract and measuring what the air was actually doing when we speak or sing, he found a totally different set of phenomena than what the linear model says should be going on in there. So he writes it up as a paper, writes up proposals, does the whole bit, and the work gets shot down. His proposals are reviewed by the fellows who are working on the engineering agenda. He's working on a different agenda: Agenda Two—the scientific agenda. He says, "Look, let me get the physics right first. Then once I understand what's physically going on in this generation, then I will worry about the mathematical modeling after that, because then I will have much better guidelines as to how to do the modeling and which approximations are meaningful and which ones are not meaningful. So Teager works on Agenda Two. He writes his papers on Agenda Two, but they get reviewed by Agenda One fellows, and the papers are shot down."
if you listen to somebody talk on the telephone, it only takes a second or so of conversation for you to know who is talking, in addition to what was said. If you try to do that analysis spectrum-wise, you'll find that you can't. But this approach is doing it just fine. Why? Because one's ear is looking at the modulations. It's a modulation detector. It's a transient detector. It's not simply a spectrum analyzer. It's a lot more. So the signal processing algorithms that you have to have are ones that can look not only at the amplitude modulation that's going on, but also the frequency modulation that's going on...........
Can you tell me what you think are the big questions, the big areas, in signal processing today?
Kaiser:
The nonlinear stuff and the time varying stuff are still very hot areas. I also want to stress again that this is an engineering agenda versus science agenda issue. It has become so easy to do so much computation using computers that people will press keys on the keyboard without thinking what they are doing. I think that there has got to be a resurgence of work on the scientific end of things. It's so easy to generate a tremendous amount of garbage that you've got to understand what it is you're doing. So it is very important that we get back to basic understanding, get a much better grounding of what science underlies the phenomenon we are looking at. We have got to be able to do that. I mean, this world is not an ideal world. It's time-varying and nonlinear. That's the first message. The next message is, the young people—or anybody, really— who are using these tools have got to understand thoroughly what assumptions underlie the tool that they are using. That will tell them what they can expect to get out. You want to know what the guts of the filter are so you can know what you have filtered out and what will pass through that filter. You've got to know that. Issues such as quantization, and, even more so, linearity versus nonlinearity.
At the risk of this being a self-referencing monologueSome people try to deny this illusion & even suggest that what we hear is a placebo but placebos work!
At the risk of this being a self-referencing monologue, I wanted to say something else - if we define a placebo as something that provides an apparent improvement for as long as we believe in it i.e until something crops up that makes us question it, then science is all placebo - a belief in theorems that improve our understanding until some new theorem is presented that replaces the old
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Frantz, it's a kinda joke with a modicum of truth which I hoped would get a laugh
Only in hi end audio, religion and some politics is there such a blatant disdain for science. Frantz, your emoticons are spot on. Pathetic.
When I talk to a few of my friends at Murray Hill, my colleagues in the speech group there, about the signal processing on speech, and I say, "Consider the speech wave." Well, the first thing they want to do is look at it through the DFT (Discrete Fourier Transform). The DFT is basically a linear tool! It's like you put on the "linear glasses," look at it, and say, "Well, I see this looks quite like a linear system." Well, that's all you can see through those tools. You've got to have a different tool to see the modulations in detail that abound in the speech wave. That was the big impetus for us working on what we call the energy algorithms, and the energy separation.
I don't know the answer to my next question as I haven't read the full 80 pages of this thread but as a sampler of what the focus is in current thinking about audio measurements - how many times has time domain been mentioned compared to the freq domain in the last 80 pages?
Ah, go on, any hints as to the answerJohn, it is for you to discover by reading the entire thread (it ain't that really long anyway). :b
Bob
Ah, go on, any hints as to the answer![]()
So they both get equal billing? Great!I don't remember how many times one over the other was mentioned. :b
A valuable contribution that measurements make is that they help separate the wheat from the chaff.
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