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incest wrote: Programmers are, let's face it, completely nerdy compared to the general population. My dad, for example, writes e-mails in all capital letters.

My dad is a programmer and he still writes emails in capital letters.

Mind you, he did do most of his programming in the 1950s, before lower case letters were included in standard character sets. So I guess he sees lower-case as a somewhat unnecessary modern luxury.

He still writes economic models in QBASIC. In capitals, of course.

Ah, so what you want is an Apricot PC with a 2x40 character LCD display (scroll down) from 1983.

This was my first experience with PCs; my dad had one, issued to lecturers by Wolverhampton University. British built by Brummies, fact fans. Just like those lovely Mini cars. Bostin'!

In my Machine Learning class at Portland State U, we've been using the Netlab toolbox from Aston University Neural Computing Research Group, which is a set of Matlab libraries and programs. I haven't used Matlab's own neural network tools or done any of this stuff in my working life, but NetLab is at least a good learning tool, and is itself GPL.

Several people in the class have speculated how much work it would require to port NetLab to Octave, but AFAIK nobody's actually taken a look. I downloaded it to my linux box but haven't tried to do anything yet myself.

From the Netlab page: "The Netlab library includes software implementations of a wide range of data analysis techniques, many of which are not yet available in standard neural network simulation packages. Netlab works with Matlab version 5.0 and higher but only needs core Matlab (i.e. no other toolboxes are required). It is not compatible with earlier versions of Matlab."

At Aston University, there are only two things you need to get a girlfriend.


Ovaries.

Slightly slow with XP eye candy turned on. Great for taking notes, reading /. www.ncrg.aston.ac.uk/~jamescj/TC1000/photos.html

At the allotted time, I turned off my main room light, and began flashing my desklamp on and off. Within 5 minutes all three towers were shimmering, including, I'm told, the faces not visible from my window.

It was a neat, if not original, social hack, and a lot of fun... This thing in Berlin is much cooler technically of course.

I'm a native English(US) speaker, but I wouldn't mind learning another language if it were the "common" one. I learned Spanish because of my wish to travel in Latin America and my desire to speak the native language of the people there.

That being said, I think that it's inevitable that English will be the universal common language. Haven't any of you seen Star Trek? Everyone in the whole damned Universe speaks English. :)

Anyhow, I'd like to see the English language morph into something more friendly to both the human and the mechanical users. The main problem that needs to be tackled is the large number of spelling exceptions. The Simplified Spelling Society has a simple solution called "Cut Spelling". Here's an example.

I'm a native English(US) speaker, but I wouldn't mind learning another language if it were the "common" one. I learned Spanish because of my wish to travel in Latin America and my desire to speak the native language of the people there.

That being said, I think that it's inevitable that English will be the universal common language. Haven't any of you seen Star Trek? Everyone in the whole damned Universe speaks English. :)

Anyhow, I'd like to see the English language morph into something more friendly to both the human and the mechanical users. The main problem that needs to be tackled is the large number of spelling exceptions. The Simplified Spelling Society has a simple solution called "Cut Spelling". Here's an example.

For your education I pulled a couple links from the web:

Site A has two pictures of a sine wave being sampled. This web page is totally wrong. They do not understand aliasing... that picture they are showing with the straight lines shows the reason that you need to have a low-pass filter. With the appropriate low-pass filter, there sine wave in the above picture will be reproduced exactly.

Site B shows the frequency domain. You're probably seen a similiar plot of the horizontal axis being frequency and vertical axis being magnitude. Don't worry about the math if you don't understand it. Just look at the pictures. The top picture shows the sampling period being less than half the period of the highest frequency in the original signal (the bell shaped thing centered at frequency 0. This is like your 14.7kHz sine wave sampled at 44.1kHz. The second is when the period of sampling T equals half the period of the highest frequency (see how the edges of that waveform exactly touch each other?). The bottom picture shows what happens when the sampling period is greater than half the period of the highest frequency. That portion of the bell shaped thing that overlaps one another is sampling noise. In other words everything that is overlapping is lost.

Site C is another site that does not understand nyquist's theorem. They are completely thinking in terms of the time domain instead of the frequency domain. Not to mention that they don't realize you always have to low-pass filter a sampled signal.

Site D actually is correct and should be understandable to even the least mathematically inclined.

• MMM Group at University of Nijmegen [publications]
• Machine Listening @ MIT Media Lab
• Affective Computing @ MIT Media Lab
• Musclefish

• Music, Cognition, and Computerized Sound, Perry R. Cook
• Music, Mind and Machine, Peter Desain and Henkjan Honing
• The Scientist and Engineer's Guide to Digital Signal Processing, Steven W. Smith
• Neural Networks for Pattern Recognition, Christopher M. Bishop

• Tracking Musical Beats in Real Time, Paul E. Allen and Roger B. Dannenberg
• A Model for Musical Rhythm, Jeff A. Bilmes
• Autocorrelation and the Study of Musical Expression, Peter Desain, Siebe de Vos
• A Beat Tracking System for Audio Signals, Simon Dixon
• Prediction-Driven Computational Auditory Scene Analysis for Dense Sound Mixtures, Daniel P. W. Ellis
• A Similarity Measure for Automatic Audio Classification, Jonathan Foote
• Representing Rhythmic Patterns in a Network of Oscillators, Michael Gasser and Douglas Eck
• Adaptive Signal Models: Theory, Algorithms, and Audio Applications, Michael Mark Goodwin
• Recognition of Music Types, Hagen Soltau, Tanja Schultz, Martin Westphal, Alex Waibel
• Irrelevant Features and the Subset Selection Problem, George H. John, Ron Kohavi, Karl Pfleger
• Beat tracking with a nonlinear oscilator, Edward W. Large
• Modeling beat perception with a nonlinear oscilator, Edward W. Large
• Automatic Transcription of Simple Polyphonic Music: Robust Front End Processing, Keith D. Martin
• Musical instrument identification: A pattern-recognition approach, Keith D. Martin and Youngmoo E. Kim
• Music Content Analysis through Models of Audition, Keith D. Martin, Eric D. Scheirer, Barry L. Vercoe
• Musical Sound Information: Musical gestures and embedding synthesis, Eric Metois
• A Machine Learning Approach to Musical Style Recognition, Roger B. Dannenberg, Belinda Thom, and David Watson
• Resonanc e and the perception of musical meter, Large, E. W., & Kolen, J. F.
• Music-Listening Systems, Eric D. Scheirer
• Tempo and beat analysis of acoustic musical signals, Eric D. Scheirer
• Content-Based Classification, Search, and Retrieval of Audio, Erling Wold, Thom Blum, Douglas Keislar, James Wheaton
• Classification, Search, and Retrieval of Audio, Erling Wold, Thom Blum, Douglas Keislar, James Wheaton