Improving Noise Analysis with the Sound of Silence 54
Roland Piquepaille writes "Researchers at Rockefeller University have built a mathematical method and written an algorithm based on the way our ears process sound that provides a better way to analyze noise than current methods. Not only is their algorithm faster and more accurate than previous ones used in speech recognition or in seismic analysis, it's also based on a very non-intuitive fact: they know what a sound was by knowing when there was no sound. 'In other words, their pictures were being determined not by where there was volume, but where there was silence.' The researchers think that their algorithm can be used in many applications and that it will soon give computers the same acuity as human ears. Read more for additional references and pictures about this algorithm."
Eh? (Score:2, Funny)
Untitled (Score:1)
Re:Ironic (Score:3, Insightful)
And if you noticed on the histographs of the sounds, that the white noise was just even distribution with small points of silence.
You almost got an easy "insightful". I certainly hope that the mods know better.
Re:Ironic (Score:1)
Re:Ironic (Score:2)
Silence is a sorely understudied aspect of communication, in general. It's such a fundamental part of everyday communication, yet little research like this is published.
What Magnasco et al. have done is to move our data processing closer to the
Re:Ironic (Score:2)
Absolutely. :-)
Really, what we mean by silence is "below the limit of sensitivity." It's all about resting the apparatus in question. And, regarding the Planck frontier: absence of evidence is not evidence of absence. Just because you can't measure it, doesn't mean it's not...
Re:Ironic (Score:2)
Sound of Silence? (Score:3, Funny)
"When asked to comment on their findings... (Score:5, Funny)
Taggers... (Score:1, Informative)
Tag this as "rolandpiquepaillespam"
Why? (Score:2, Insightful)
Re:Why? (Score:1, Flamebait)
Re:Why? (Score:1, Flamebait)
Will the other half buy it? (Score:3, Funny)
If only I could sell this theory to my wife.
Re:Will the other half buy it? (Score:2)
You won't need to - Women have understood this principle for far longer than we mere males...
"Anything wrong, hon?"
"Nothing."
Re:Will the other half buy it? (Score:2)
Re:Yes, but... (Score:2)
But on-topic, I'd say yes. It's definitely not silence...
Seems vaguely similar to dark image analysis (Score:3, Interesting)
Can you hear me now? (Score:3, Funny)
By the time True AI is here... (Score:2)
Not only that, but there have been numerous articles on the development of electronic eyes. By the time they've got all the kinks worked out in AI they'll already be able to let the new robots sense our world in the way we do. The only thing they're really missing are the senses of smell and taste. I can imagine those won't be ne
Re:By the time True AI is here... (Score:1)
Granted, it's easy enough for two people to agree that a lemon is sour instead of sweet, but where exactly is the line between the two? Will you and me (and our Robo-Taster 2000) agree every time that an object is sour instead of sweet? Maybe, if we give it a numerical value, bu
Obvious? (Score:2)
Surely there has to be more to it than that? Not only is it not "non-intuitive", it's completely bloody obvious, so much so that I already assumed that people did this in professional recording situations.
Think about it, it's just like weighing two things together, and then finding the weight of one item by weighing the other and taking the difference between the two measurements.
Re:Obvious? (Score:1)
Obvious in retrospect (Score:2)
Second, we can all very easily deduce that our interpretation of sound deals with a very low signal-to-noise ratio. How many background sounds are we dealing with constantly? How surprising is it that analyzing sound subtraction (cancellation) from the noise is as effective as analyzing addition?
I'm no hearing expert, and I'm definitely not an expert when it comes to algorhythmic sou
Re:Obvious in retrospect (Score:2)
EVERYTHING is "obvious" in retrospect. The question always seems so simple once you know the answer.
Re:Obvious in retrospect (Score:2)
42.
Re:Obvious in retrospect (Score:2)
In my statement, the article, and the parent post to mine; the question is already known. When you find the answer, the already known question doesn't seem so hard.
This sounds like Zen processing (Score:2)
Could noose for awl! (Score:4, Funny)
Actually, this might really be useful (Score:2)
I'm not sure how it would work, but he was able to determine position and distance quite well, but was having some issue with the different densities of materia
Sounds of Silence, Huh... (Score:2)
Too late (Score:2)
Original paper? (Score:2)
Re:Original paper? (Score:4, Informative)
Heck...a little bit more and you've got a Simon and Garfunkel song. Perhaps you could even explain why the words of the prophets are written on the subway wall. But I digress.
The summary is completely useless, and the article isn't much better. After reading the description 3 times, I figured out the graph, at least. X-axis is time, Y-axis is frequency, and color is amplitude, so it's essentially a time dependent power spectrum density (PSD) with anything above a cutoff amplitude shown in black. I believe the Navy uses a variation of this called a waterfall to help interpret sonar sounds. I got stuck again, though, reading their description of the sample.
Aside from the apparent infinite-energy contradiction if this were true, the graph clearly shows that the signal is both frequency and time dependendent. Obviously, that's the case they ultimately have to deal with to apply this method, but the article suggests otherwise.
As for what they're actually doing, presumably, instead of operating on a set of data that includes time, frequency, and amplitude, they are cutting it down to time, frequency, and sound/no sound. This would cut the data size by the amplitude resolution (eg, 1/16th for a 16 bit amplitude sampling). This must assume that amplitude is irrelevant to the sound, which based on my (limited) experience working with PSD's, I'm skeptical of. Perhaps the odds of getting the same digital time/frequency data for two different sources is low enough that this can be ignored, much like the likelihood of two data sets yielding the same MD5 sum is non-zero but insignificant.
Re:Original paper? (Score:2)
Re:Original paper? (Score:1)
Disclaimer: I'm a PhD student in pretty much the same field, but I'm not experienced enough that anyone should take my opinions as fact.
TFA looks interesting but overhyped to me.
Not
Re:Original paper? (Score:2)
I appreciate the need for simple summaries, but comeon, at least link to the meat of it.
The second link in the slashdot summary includes a link to the full text of the research article.
Half the time, I think people feel that science is out of their reach because the articles they read about it don't give them enough information to even start learning about it. When science is presented like this it gets reduced to blurbs with the logical content of zen koans. "sound from no sound" "noise from silence"
Can it get the 18-1/2 minutes back? (Score:2)
-dB
fantastic ? (Score:1)
Not worthy of a press release (Score:1)
do not have anything worthy of a press release. They distinguished a pure tone from noise,
which is a very easy thing to do. Other than that, they just have a pretty picture. It's
not useful for anything.