Three new papers on Instrument ID

My supervisor Mikel Gainza presented at ICASSP 2009 in Tapei, Taiwan. Thanks Mikel for the heads up on three papers on musical instrument ID:

• "Instrument Analysis and Identification of Polyphonic Music using Beat-Synchronous feature integration and fuzzy clustering", Soo-Chang Pei, Nien-Teh Hsu
• "Polyphonic Musical Instrument Recognition Based on a Dynamic Model of the Spectral Envelope", Juan José Burred, Axel Röbel, Thomas Sikora
• "Interpolating Hidden Markov Model and Its application to Automatic Instrument Recognition", Tuomas Virtanen, Toni Heittola

Very useful maths tutorial website

In his own words, this maths tutorial site by Paul Dawkins offers a "complete set of free online (and downloadable) notes and/or tutorials for classes" he teaches at Lamar University.

Very useful indeed! Thank you Paul Dawkins.

And not to forget the Linear Algebra course...

I forgot to mention the Linear Algebra course from MIT. Although it's long and somewhat challenging, the lecturer clearly illustrates some of the fundamentals of linear algebra. The notion of basis vectors has become a lot clearer to me and as the lectures progress, other areas become much more conceptualised. To be honest, these lectures were a chore at the beginning but as the lecturer paints the picture, I can visualise the notions involved. Very interesting and highly recommended. I just have to snigger at the fact that I've had to venture into another domain of mathematics and jot down my experiences in my 'travel' log. AMII is most certainly a journey of the intellect.

MITOpenCourseWare - some excellent material

Below are some excellent online courses from the MIT Open Courseware. There are many courses available. I've selected a few here which are related to my own research:

• Introduction to Java
• Machine Learning
• Discrete-Time Signal Processing
• Automatic Speech Recognition
• Acoustics of Speech and Hearing
• Brain Mechanisms for Hearing and Speech
• Neural Coding and Perception of Sound
• Pattern Recognition for Machine Learning
• Introduction to Neural Networks

The bible on pattern recognition

I came across an excellent book on pattern recognition: Neural Networks for Pattern Recognition by Christopher M. Bishop. I've actually enjoyed reading this book thus far. While other books on the same topic have been overcomplicated with mathematics, this book simplifies the concepts with good wording alongside the required mathematics. An excellent reference in my opininion. For example, the 'Curse of dimensionality' is explained very well, much more so than any literature I've come across in my research.