The thing that interests me the most
about signal processing is the
potential application in music. I
remember a while ago I saw a preview
of an application (Sorry, forgot the
name)
Maybe cubase ?
which could listen to a recording of
someone playing a guitar, and
automatically graph it out across a
time-line with the actual notes/chords
that were played
Deeply simplified, when you play a note you produce a periodic wave with a given frequency. There's a mathematical trick (the Fourier transform DFT) that converts the wave into the spectrum, which instead of presenting intensity against time, it shows it against frequency of the wave. For example, a perfect A note from a tuning fork would produce an oscillating wave at 440 Hz. In the time domain this would appear as a sinusoidal wave. In the frequency domain, it will appear as a single, narrow spike centered at 440 Hz.
Now, when you play a guitar you don't produce perfect sinusoidal waves. Hitting an A will produce the fundamental frequency, 440 Hz, but also a lot of additional frequencies (e.g. 880, on octave higher, but also a lot of other higher and lower freqs), due to the physics of the vibrating string, the material and shape of the guitar etc.. These additional frequencies are called harmonics, and they mix with the fundamental to produce "the sound of the guitar" (what in musical jargon is called timbre). A different instrument (say piano) will have different mixing of harmonics with the fundamental, producing a different timbre.
What DSP programs do is to perform a DFT on the entering signal. With additional tricks, they find the fundamental and the harmonics, and according to what they find they infer the note you played. This must happen fast, because you could find the note while playing live and triggering special tricks. For example, you could hit an A note on the guitar, the DSP understands it's an A and replaces it with the A from a piano, so from the speakers you obtain the sound of a piano.
Using the program, the user was able
to move these around and even edit
them. Now, obviously this is a lot
more complicated, but does it involve
the same thing? Signal Processing? I
am also interested in possible
applications in music visualizers and
intelligent lighting systems.
Yes. Once you are in the frequency domain, things gets very easy. For example, you could light up a specific light according to the voice frequencies, and another light with the bass drum.
My understanding is that doing this
processing on a compressed audio
format such as MP3 wont yield the same
results as MIDI which contains
separate tracks (Maybe I
misunderstood).
They are two different things. MP3 is a compressed format from a sound wave. Basically it takes what pilots the speakers, and compresses it. The idea is the same: DFT, then removal of stuff that is unlikely to be heard (for example, a high pitch that comes right after a high intensity sound is less likely to be heard, so it gets removed).
MIDI on the other hand is a scroll of events (you know, like those pianos in the far west, with the rolling paper scroll). The file contains no music. It contains instead directions for a MIDI player to perform specific notes at specific times with specific instruments. The quality of the "instrument bank" is (among other things) what distinguish a bad MIDI player (which sounds like a child toy) from a good MIDI player (which sounds realistic, in particular for pianos and violins, for wind instruments I still have to hear a realistic one).
It takes that going from MIDI to MP3, you just perform through a MIDI player. To do the other way around is a different story altogether, and much more complex, and here is where DSP comes into play, as you said.
It's like boiling a fisk tank. You get a fish soup. But to get from the fish soup back to the fish tank, it's much harder.
Would an uncompressed
format such as PCM do better than MP3?
PCM is a technique to convert an analog signal to a digital signal. So your question has a fundamental misunderstanding, that no PCM format exists (the RAW format is a close call, contaning basically nothing but crude data). If you ask if a uncompressed WAV (which contains PCM data) is better than MP3, then yes, but the question sometimes is how much this better really matters to the human ear, and how much postprocessing you have to perform on that data.
know if there are any existing
libraries which can facilitate this,
or articles pertaining to this subject
that geared towards Computer
Science/Programming, with perhaps
example code. Even open source
sound/music visualizers or any other
open source sound processing code
would be great.
If you like python, take a look at this page
Sorry if I didn't make any sense. Like I said, I don't know what I'm talking about.
Neither do I, but I toyed a bit with it.