> The original point stands -- the higher resampling rates are increasingly pointless.
But the mp3 tracks are not resampled. They use the same sampling rate of 44.1kHz, with a variable bit-depth. So, for instance, a 320kbps mp3 file still has the original sampling rate of 44.1kHz.
- The sampling rate, measured in kHz, for CD tracks is 44.1 kHz (not kbps), which determines the frequency range. Nyquist-Shannon applies to this.
- The bit-depth, number of bits/sample, determines the SNR of the signal, but has no effect on the frequency range. A typical audio CD has a bit depth of 16.
- The bit-rate, measured in kbps, is (sampling_rate * bit_depth * channel_count). So for an audio CD, this would be 441000 * 16 * 2 = 1411200 bps = 1411.2 kbps.
Nor did you get to drive it in the demo - it's the scripted composition that makes it interesting. It works brilliantly as a demo, but I suspect the novelty of driving the morphing squigglemobile in the demoverse would wear off pretty fast.
And once you do start incorporating other elements like AI, level geometry, physics, etc to make things interesting, you'll no longer have the luxury of allocating all your computational resources for graphical eyecandy.
The creativity is in no way restricted to the demoscene - just more conspicuous. Creating games involves a different set of constraints, and there are plenty of examples of ingenuity & creativity to be found there.
In fact, many game programmers are also involved in the demoscene; For instance, the guys who created Max Payne were a part of the infamous Future Crew.
I wouldn't say acoustics is dead at MIT. I participated in some very interesting ocean acoustics research in the last few years which has been published in Science a couple of times.
For anyone interested in reading about underwater ocean acoustics:
When I was studying Electrical Engineering, I noticed that SVDs cropped up frequently. From memory, a quick list of situations where I can recall using SVDs :
- For computing homographies in Computer Vision
- For clustering in Machine Learning
- For noise reduction in DSP
In general, when the problem can be modeled using linear algebra, SVD often proves to be a versatile tool ( for linear optimization, eigenvalue problems, least squares, etc).
However, that doesn't make it the most important topic. I could just as easily name a bunch of other techniques that are also frequently used : FFTs, Linear programming, Monte Carlo methods. If you're an electrical engineer, all of these would be useful. Yet, I wouldn't call any of them the most important topic.
But the mp3 tracks are not resampled. They use the same sampling rate of 44.1kHz, with a variable bit-depth. So, for instance, a 320kbps mp3 file still has the original sampling rate of 44.1kHz.