On Saturday 23 February 2008, Sebastian Tschöpel wrote: I think that would be a matter of training, basically; much like many people can "hear" what something will sound like before playing it. Untrained ears have a much harder time picking out and following individual instruments from a mix, but this is something that improves over time, in my experience. It's a lot like learning to read by recognizing the words rather than the individual letters. It would seem like the same basic mechanisms at play, though I don't know if that's actually how the human brain implements it. Now, theoretically, if you can make out the individual instruments from a mix, and know what these instruments would sound like on their own, you should basically be able to recreate any combination of these instruments in your mind. Anyway, from the strictly technical POV, there's this major overlapping problem. Getting the individual frequency components out of a fragment of music is trivial (relatively speaking) - but how do you know which ones go with what instrument? Well, consider a simple example with a solo melody voice over a simple bass line. As long as you can make out the fundamentals (which can turn out to be quite hard enough in real applications!), you can look at the spectrum and figure out which components follow what melody. This would require multipple analysis passes (to learn the instrument sounds and melodies), and/or a database of "familiar sounds"; it's not something you can just do frame by frame on unknown data. ...and of course, there's a million "minor" issues around this that make it a lot harder than it appears to be. Logically, it has to be possible, but maybe the first step would be to dispell a few confusing myths about how the human brain does this stuff. I think the brain has access to a lot of data that algorithms of this sort generally don't have. For example, I don't think it's realistically possible to do this without some sort of database of "familiar sounds", and/or some model of how the "average musically trained" human brain infers fundamentals from audible spectra. (There are plenty of instruments that have very little energy around the fundamental frequency, which makes even "simple" pitch tracking non-trivial.) Considering how the brain appears to work, I don't think there's a strict distinction between a "database" and a "model" in this regard. A neural network might be the proper model for software, and it's state after appropriate training would be the "database." //David Olofson - Programmer, Composer, Open Source Advocate .------- http://olofson.net - Games, SDL examples -------. | http://zeespace.net - 2.5D rendering engine | | http://audiality.org - Music/audio engine | | http://eel.olofson.net - Real time scripting | '-- http://www.reologica.se - Rheology instrumentation --'

What kind of music? Do you currently have a certain song? How long do you play? 1st way: Try to find transcriptions (not tabs) 2nd way: Slow it down! E.g. use audacity to slow down the track while keeping the pitch (time-stretch) 3rd way: Use the space during the notes you can't recognize to add your personal note - that's what i do if it's not classical music. -- Best regards, Sebastian. Web: linuxaudioblog.sternenhejim.de On Sat, 2008-02-23 at 15:50 +0100, schoappied wrote:

On Saturday 23 February 2008, schoappied wrote: [...] Anyone here *not* spending too much time with computers and stuff...? :-D Anyway, first you say we're underestimating the complexity of the human brain... ...and then you want machines to do what not even your brain can handle? ;-) Well, structured audio, or just multitrack + mixdown metadata would do the trick, but realistically, that only really works if you author specifically for that format. Also, as soon as you want any non-trivial processing in addition to the mixing, you either have to apply that destructively to the tracks, or you run into CPU load issues in players, problems finding effect implementations that you can legally include in your files etc. It's definitely doable (the technology is there already) and very interesting, but I doubt it'll ever replace plain, "opaque" streams as a general music distribution format. //David Olofson - Programmer, Composer, Open Source Advocate .------- http://olofson.net - Games, SDL examples -------. | http://zeespace.net - 2.5D rendering engine | | http://audiality.org - Music/audio engine | | http://eel.olofson.net - Real time scripting | '-- http://www.reologica.se - Rheology instrumentation --'

On 23/02/2008, schoappied <schoappied(a)gmail.com> wrote: Sorry for the late reply : I was having some time off the PC .... Acknowledging the marvelous complexity of the human brain should not prevent us from putting this wonderful resource to use, and think up new ways of doing new things ... , should it ? Incidentally, this is how mankind has thrived during the last few millennia: by taking hints from nature, and designing tools and machinery. In turn, this machinery (instrumentation, etc ) has helped increase the amount of knowledge available for our brains to consume. Anyway, I am probably just preaching to the converted, as it is you who - in the first place - asked whether such a computer program already existed ? Thanks to David O. and others who shared their thoughts about how this remote perspective could become real, despite the current state-of-the-art in DSP forbidding to separate a mix of overlapping frequencies. Kind regards.

On Sun, 2008-02-24 at 17:20 +0100, Jean-Baptiste Mestelan wrote: IMO the way to do this would be to write a program that leveraged several of the LAD resources. You would need a fairly hefty setup to achieve it in realtime. It would need to learn how to extract the parts from the composition and recreate them with the standard tools. I imagine some kind of genetic based learning algorithm would help similar to the work done on the self driving cars, the robot face or the Lord of the rings animations. It would be a very interesting project and a great addition to a CV but you might need a couple of years and some serious cash to achieve any significant results... Cheers. -- Patrick Shirkey Boost Hardware Ltd.