Old 17th May 2008, 12:42   #41
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Old 17th May 2008, 12:49   #42
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Old 17th May 2008, 16:12   #43
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seems you forgot to add one texture
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Old 17th May 2008, 17:40   #44
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Old 18th May 2008, 01:41   #45
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which?

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Old 18th May 2008, 01:46   #46
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Old 18th May 2008, 10:37   #47
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the who'r'us one
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Old 18th May 2008, 15:38   #48
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Old 18th May 2008, 17:59   #49
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that's what i was thinking it might be, i believe it's in a previous zip, though i don't know which offhand

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Old 18th May 2008, 18:47   #50
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found it (sphinxwhorus.jpg) in bing crosby.zip uploaded 5/9 on page 1 of this thread

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Old 18th May 2008, 18:50   #51
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Old 19th May 2008, 20:50   #52
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I heart ak-47

I was visited by some very friendly aliens this weekend, they will be missed
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Old 20th May 2008, 03:37   #54
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^^I dig it and thanks for the reccomendation, I'll be checking myself in tomorrow... For now take a sip of my purple orange juice
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Old 20th May 2008, 07:08   #55
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Old 20th May 2008, 11:09   #56
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Old 22nd May 2008, 08:51   #57
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Old 22nd May 2008, 20:08   #58
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Old 23rd May 2008, 21:58   #59
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viz

i guess i should start a new thread for this, but i don't want to because i am being irrational about it. in fact, i'm sure there is a lot better place on the net for this, but i don't know where yet.

i want to build a new visualizer, but the only way i would set out to do it would be to do some serious research / and/or build some complex timbre analysis / synthesis software. i have thought a lot about this in the past, but was always too indecisive to start for this and that reason. now, i may have thought up an approach i really can get started on. i'd like to use genetic algorithms to break down different types of music or maybe even specific songs.

there is an information loss once you mix two or more timbres/instruments/voices together. this is made up for by our ear and our brain's extensive neural net. to approach the complex task the brain does so easily after so much life training, i propose using a genetic algorithm to generate weighted feature vectors. the feature vectors might start out being the typical ones used in acoustic research, like spectral centroid, noise clouds, etc. however, the point of a genetic algorithm is to evolve something according to constraints. you must allow for mutation and kill off (select) the worst performers (according to your constraints). the mutation algorithm would be the hardest part i am thinking. (like, are we talking random math expression mutation or something? shunting yard algorithm backward?) i think somewhere in there you'd also need statistical comparison to really do voice separation, and thus a huge buffer if you want to end up with a real time visualizer, either that or a tapering, where the visualizer will suck at the beginning of any new timbre introduced, and then perform well after it figures out which voices are which.

the point then, after voice separation, would be to taylor visual events to the individual voice events. you would then give the preset writer the opportunity to map any of the generated feature vector's parameters in any way they want to the individual voice's visual representations. however, this will limit a universal application of a preset to any song, because the vectors are taylored to the song/album. therefore, i would further propose another more general layer of instrument classification so a preset could go more distance than one song/album.

you'd then want to also store the generated vectors, perhaps by album or song, to reference in the future.

this could lead perhaps as well into real-time random music generation based on a load of songs. one might also parlay that into biofeedback / self-directed virtual (albeit abstract) reality of sorts. (i think the brain tends to like low prime number ratios when it comes to almost anything - including tension models - micro and macro structure of sound / viz, but with such a biofeedback device, one could really pin down the to the individual and perhaps even their mood what ratios would be most useful at a particular time)

of course, this probably sounds daunting, but there are plenty of examples out there in research literature that are being applied to various aspects of speaker recognition and music analysis. someone is probably working on something similar already. if anyone who cares finds or already knows of an open source project out there for something like this, feel free to come to my home and sock me in the stomach, or maybe easier yet, post to this thread your dormant (to me) knowledge.

all that said, i don't think i am the one to build such a thing, as i am too scatterbrained to pull it off, though of course i'd endure torture for the luxury to direct it's creation.

oh, and here are my latest useless presets. i hope someone is enjoying these. i guess if ppl are downloading them that must mean something, probably they like downloading and then immediately deleting files. i hate how when i create something, i like it, and get all excited, hoping people will give me all kinds of glory, and then after thinking about it a while, i hate what i created and myself. i think maybe if god exists, this may be the dysfunctional type of relationship he has with his multiverses. i only dare compare myself to god if he is a slob i guess. it is such an uncomfortable set of tradeoffs being raised an lds bastard. not what i'd call an optimum tension model.
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Old 24th May 2008, 00:11   #60
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stfu
http://youtube.com/watch?v=Ozwl4SsdCus
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Old 24th May 2008, 00:28   #61
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take a bath in your misery

http://de.youtube.com/watch?v=JWVshkVF0SY

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Old 26th May 2008, 19:53   #62
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LMAO, did your fridge go out or something flexi? Smelly fish and cheese, then moving to the unspoiled dessert.... Anyway, I love pretty much everything from your 5/23 pack suksma (particularly petalcide!!!), and most of your other releases for that matter (just check the names that make their way to the names of my mashes )...However, my rampant ADD makes it nearly impossible to remember who/what I like, but I have been organizing my preset library lately which makes it a bit easier. But like u sed, if people download it, there's usually a reason

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Old 26th May 2008, 20:18   #63
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^^^As a matter of fact, let me destroy that one for you

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Old 26th May 2008, 20:48   #64
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Here's one from flexi's 'bon appetite'...and this may be so boring that prolonged exposure to this preset may cause you to fall out in to a hypnotic state in which you are compelled to confess your addiction to milkdrop

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Old 27th May 2008, 23:58   #65
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Quote:
...but was always too indecisive to start for this and that reason. now, i may have thought up an approach i really can get started on. i'd like to use genetic algorithms to break down different types of music or maybe even specific songs...
sorry but if i hear something like that, it sounds like blablah. i am very much interested in results.

Do you have any experiences in modelling such systems?
(i don't have either...)

Quote:
i propose using a genetic algorithm to generate weighted feature vectors
.
my knowledge of signal theory is only scratching the surface, but i have my dear doubts if this is the correct approach.
What should your 'feature vectors' express? a violin, a male voice, the frequency spectrum, a punch in your face? i have no idea what you are talking about.
I guess, what you are looking for, is a so called '(recursive) auto-associative memory' - but first: this is not genetic programming. and second: these memories/nets have to get trained with a lot of samples of what you want to detect and afterwards you can only hope it actually learned what you wanted it to learn

http://www.ra.cs.uni-tuebingen.de/so...welcome_e.html
(this software visualizes the activities of neural nets - check it out)

Quote:
the point then, after voice separation, would be to taylor visual events to the individual voice events.
okay this is design. - no this is art! - here's the point where a human can project a mood (expressed in motions and colors) on it.

i suggest you to do your theoretical homework in neural nets (headword backpropagation), clustering algorihtms, and of course auto-associative memories
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Old 28th May 2008, 03:48   #66
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gen alg

i have no experience with genetic algorithms, and i am not wanting to use neural nets. it's just that the idea seems appealing when applied to timbre.

for gen alg, some of the applications i've read about have been to design things for systems that are more of artsy science, like, for example, antenna design or virtual world tasks like optimizing jumping, or getting a set of shapes with restricted movements to walk. i've seen enough examples of it mags or shows like popular science or nova to know this has been used a lot for various things.

http://www.popsci.com/scitech/articl...machine?page=2

http://www.pbs.org/saf/1103/video/watchonline.htm
3rd vid down

if you search genetic algorithm on youtube you'll get a bunch of results too if i remember correctly

to use another more typical example of something that uses features, one can look at ocr (optical character recognition) technolgy's use of height, lines of symmetry, etc, anything that teases out the common attributes of a letter.

features for sound would be things like spectral centroid (for which there is a simple equation for a set of temporal and spectral data - many to one mapping). the point of using a mutating feature vector is that researchers don't really have a way of knowing how we separate sound, and we probably all do it a little differently anyway because we don't have all the same exact structures/genetics/experiences.

some tools exist (though not freely available) to do sound synthesis using typical features like:

Log-Attack Time
Temporal Centroid
Harmonic Spectral Centroid
Harmonic Spectral Standard Deviation
Harmonic Spectral Deviation
Spectral Centroid
Harmonic Spectral Variation
Amplitude Envelope
Average Harmonic Spectrum
Non-Harmonic Spectrum
Spectral Flux
...tons of others

Researchers are limited to these only because of trial and error. why not leave the trial and error up to the computer? or better yet, through the computer, mediated by a hearing human? So, if they change one features of the above and leave the rest the same, their measure of 'how useful is that feature?' is how much the volunteer perceives a timbral change (like i changed spectral centroid by 1 unit, what is your perceived timbral change from 1 to 10?). However, there could be a lot better features out there, but we just have run across that way of manipulating the spectral data (or other domain - doesn't have to be spectral).

The genetic algorithm would mutate the above primer features in some arbitrary fashion, and then select out the bad as specified by whatever one wants. you might want to automate the selection process by specifying constraints. or if you wanted to have a person do the selection manually that maybe the best way depending on the application. it would take a long time, but once it's over, you have the ability to change the viz with the sound in the most dramatic and/or correlative ways, if that was your goal. it's not training that takes time, but selecting. if it was a neural net, sure, you'd take time training it. it may be wholly impractical to attempt to select manually because of sheer numbers, unless you can make it an "as you go" type selection, like when you rate a preset, or better yet, the computer measures your thought response. if you did automate it in the computer, though, you'd then have to be very clever about how to automate it. i mean, you can't really build a fully trained brain and cochlea in the computer, and if you did, you wouldn't need to bother with all this anyway. so, it's not like i've got this worked out from beginning to end. some thought would have to go into the selection and mutation process.

i'll check out your posted link in a bit because that definitely sounds interesting.

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Old 28th May 2008, 11:06   #67
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i have already known the video, see this page:
http://www.spiderland.org/

and as for education, here an insider tip:
http://www.framsticks.com/
some years ago, i managed to create different kinds of runner, and let evolution make them faster.

so i think i have a clue of the topic, but what i don't understand is how and WHY you want to realize a feature detection by genetic approaches. i guess it is because of the great populism and the prejudice these systems can handle every problem. But if you have ever tried it yourself, you would have noticed there is no ghost in the machine, that liberates you from designing algorithms yourself.
it is one thing to find an optimized circuit design, but we are talking about music - a kind of art.
i think you missed the point that a human has an intuitive feeling for trial and error (maybe by experience or genius, but all in all by nature) "hey change this parameter here a bit, now there, no wait, take this back, wow!"
now a computer is really fast ...ridiculous fast in its dumbness

what do think would a genetic system produce if you say "here are some lego-blocks now build me a castle" ?
and maybe you think you are clever and you define alot of constraints (room height/width/amount, window size,...)
the thing is, you will only get Neuschwanstein, if you have specified it straight forward to every nut, bolt and screw

but i don't want to argue with you. show me this isn't all hogwash. I am just sceptical because everybody talks of it, but it seems results are too confidential.


never mind
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Old 28th May 2008, 17:33   #68
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oh i'm sorry - i had the pic in the cache and didn't see the server denies it
damn... now it's too late to edit (mod is called)
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Old 29th May 2008, 07:37   #69
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i have to agree with you on all points, even on of the genetic algorithm youtube things applied to architecture i had seen was exactly that; it couldn't get it to build anything ultimately aesthetically pleasing no matter what constraints they tried. there really isn't going to be anything but some alien intelligence that could match what we do as humans. but, then again, maybe we are just part of a vast simulation who's purpose is for us to produce insane art, in which case i guess that's what i usually do because i'm too lazy to try to get a computer to do it in its dumbness. i guess what i was wanting to create, as should always be the case in computer science, something that exploits the dumb fastness of the machine, while leaving as much of the artistic parts up to the human. so that's excellent design advice. and i was thinking a lot about the (paraphrasing) 'i want results' statement. it's true with me too. having to labor under some huge project that may ultimately look like shit or be wholly impractical is not what i want. but then you have to admit, designing a piece of software is an art all its own. i mean, in some sense, that's all algorithm or endeavor sort of is. you can be rigorous to find tautologies (equivalences) like in wolfram's work, for example. but stumbling onto things is what is in most artist's blood, including mine, because i am not very tolerant of rigor. anyway, point taken. but i'm addicted at some level to thinking about what might make a better system, because i'm always wishing for something, for example, that milkdrop doesn't have the ability to do. (and not necessarily impractical things)

that said, i'm only wanting to evolve features of timbre and let the user choose what evolved features most significantly affect timbre. i would think nature embedded in us at least some hard-wiring that we could flesh out as to what parameters, when adjusted, correlate to perceived differences in timbre, whereas, many arbitrary features, when adjusted in some direction, don't result in any perceived timbre difference, and thus can be more or less thrown out, the same way a jpeg exploits our inability to perceive some things about images, and thus saves a lot of space. but who knows, there may be so many features that a small orthogonal set doesn't exist.

there is an mpeg7 standard being developed. some guy named daniel mintz had his thesis up online at one point. he used the mpeg7's standards for timbral features to create some timbral synthesis software, which wasn't released due licensing of some of the apis he used. i'd post the thesis here, but it's too big. i have no idea how well it worked, but i had wanted to play with it, though it could only create one sound at a time. to make it more artistically useful, one would probably want to create several a whole instrument at once. i could probably post it somewhere if you are interested.

anyway, here are some more presets which i did a few days ago and last night, but was too tired at the end to post. hopefully they aren't altogether fugly.
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Old 29th May 2008, 11:45   #70
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Old 29th May 2008, 13:59   #71
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Quote:
designing a piece of software is an art all its own
correct, that's why i spoke out my doubts.

Quote:
but who knows, there may be so many features that a small orthogonal set doesn't exist
it may be fun to find out.

I believe what is most important to visualization, is not to rebuild timbre features (in order to recognize em), but separating each from another. Here's a quick idea how to approach (i may be totally wrong, this is pure intuition).

let's say you want to distinguish 4-5 voices - each of them will be represented by a special visual feature (an artist can wire these to the final output).

say this is a feature vector:
Quote:
Log-Attack Time
Temporal Centroid
Harmonic Spectral Centroid
Harmonic Spectral Standard Deviation
Harmonic Spectral Deviation
Spectral Centroid
Harmonic Spectral Variation
Amplitude Envelope
Average Harmonic Spectrum
Non-Harmonic Spectrum
Spectral Flux


imagine you can buffer this vector
- at a sample rate for which i don't know what is a practical value
- for a period of time for which i don't know any practical value either (estimated between 10 and 60 seconds)

so once you have this set of very actual timbre feature vectors, you let a clustering algorithm run right over it.
(machine learning by concurrency)

et voila, there are your reference vectors. then you can experiment with a similarity measure
(i don't know if a simple eucledian distance norm will do it - the same applies to the distance norm used in the clustering algorithm)

and all in all, i can't say anything on realtime-behavior. worth a CUDA implementation?

hope i could help you a little don't worry about the genetic approach, but that's like you're using a sledgehammer to crack a nut.


Quote:
maybe we are just part of a vast simulation who's purpose is for us to produce insane art
I'm totally convinced of that

[OT]
Quote:
i have to agree with you on all points, even on of the genetic algorithm youtube things applied to architecture i had seen was exactly that; it couldn't get it to build anything ultimately aesthetically
don't know such videos, can you point me to one?
[/OT]
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Old 30th May 2008, 19:01   #72
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i'll try to find some of those vids a little later today if i don't forget, no time now, here are more presets i guess
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Old 3rd June 2008, 05:56   #73
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Old 8th June 2008, 08:19   #74
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more mash, a couple of spectro fiddle
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Old 8th June 2008, 15:14   #75
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Old 8th June 2008, 16:34   #76
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Old 10th June 2008, 13:15   #77
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Old 11th June 2008, 20:39   #78
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Ooooh I love the new screen!!! Still no pixel shaders, but some of these would be really cool if anybody feels like texturing them
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Old 11th June 2008, 21:52   #79
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this came out nice. Uses the first preset collaboration, too!

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Old 12th June 2008, 10:55   #80
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Go Back   Winamp & Shoutcast Forums > Visualizations > MilkDrop > MilkDrop Presets

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