Okay, if you and your friend agree on which 5-sec
portion to compare (e.g. always use the first 5 sec, not
counting any initial silence that might be present), then
you have a fairly good chance of building a DFT-based
discriminator/identifier with a pretty good success rate.
In this case, Perl could be very
handy for driving the DFT/VQ engine on your friend's
audio file, doing data reduction on that output, and
running or maybe even computing the suitable statistics to
identify a "best match" in your local database of first-5-sec
snippets.
Just building your local database of "song signatures" will
be a very instructive exercise, and you can use it for both
"training" and "testing". I could go on... but it would all
be speculative, and you should work it out for yourself. | [reply] |
If you allready have the file on your server, is it the same sample, f.i. extracted from the same music CD with same sample rate? Or might it be two different samples from two different LP records? Or could it be f.i. two different recordings of Beethoven's 5:th? | [reply] |
I will have some files in a database the second file, or the file to compare will come from my friend server, and he will get tht files from an radio stattion with his computer.
In fact it sounds dummy but the objective of my project is that the computer will do something when it "listen" the right song, exactly the same way when you wait for a song to play in a radio station, when you listen to it you will think "Thats the song i was waiting for", well i want to computer to do something like that :)
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Optimus magister, bonus liber
| [reply] |
Ok, got it! If one computer listens to the radio, which is an analog signal, the computer does the A/D conversion. If there are other wav files created by other computers in the same way, the digital data in the wav files differ, even if the sound source would be the same music CD. We have to stick to Fourier analysis and that kind of things.
| [reply] |
