Slashdot Mirror

• 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

• 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

• 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

2) Erlang is an open-source functional language for distributed systems, released by Ericsson. They developed it to run their networks, and say it cut their development time by about 90%. Check Google, all my links appear to be slashdotted or something.

Check out Concurrent Clean. I ran into this functional language during my senior year in university, and I haven't really kept up with it since, but it has several very interesting features that may help to make functional languages more widely accepted. It addresses the first two points you listed as reasons functional languages aren't popular:

• It supports destructive updates (i.e., analogs of the assignment operator in C or C++) and yet still is purely functional. (If you don't see how this can be true, visit Clean's homepage and find out!) What's the big deal about destructive updates? Well, one of the reasons functional languages have been slow is that, in order to preserve mathematical consistency, all data objects must be immutable, and therefore, everytime you perform an operation a new copy of the object must be made. However, Concurrent Clean implements a very clever way of allowing destructive updates in a way that doesn't affect its mathematical correctness. This results in very impressive speedups (I think they claim that many Clean programs are as fast as a C/C++ equivalent, but you've gotta verify that yourself).
• Concurrent Clean has a very nice I/O system that, OT1H, fits perfectly in the functional paradigm, and OTOH, isn't contorted into an odd, un-intuitive paradigm that most functional language I/O systems are. When I did my research on Clean years ago, you could basically implement an entire GUI declaratively, in a format that's very easy to read, and very easy to understand, and yet, still totally "purely" functional. I'm sure a lot more has happened to this GUI library since then. I strongly suggest anyone interested to check it out! :-)

Now, I admit I'm biased (mostly due to having done a lot of research on Clean years ago), but Clean looks like one of the promising candidates for widespread adoption by the industry. Or at least, I'd be so bold as to say that the above two mentioned points are things that I'd probably expect a widely-accepted functional language to have.

I wouldn't shell out $600 dollars if I couldn't listen to NPR on the way home.

Actually, the frog thing was an experiment at the Nijmegen High Field Magnet Laboratory in the Netherlands. They have a video on their webpage, http://www.sci.kun.nl/hfml/froglev.html , and a very interesting letter they got concerning their experiment.

However, I am afraid access to the page currently is very slow.

Dude (Dudette?)...

The possiblity of being identified by a serial # over the net has been a possiblity for _years_.

Checkout this site if you have a network card and know its hardware (MAC) address. Should be something like 02:06:82:45:34.