How not to write a PhD thesis

A great article in the Times Higher Education on 'How not to write a PhD thesis' by Tara Brabazon (Professor of Media Studies, University of Brighton.)

Need help understanding statistics?

I found a great video based tutorial on interpreting statistics by The Teaching Company. Information on the tutorial can be found here. It will only cost you close to 250 USDollars!

A listing of some new relevant publications

A listing of some recent publications (well most are recent) relevant to my own research area:

• Brown, J.C. and P. Smaragdis (2009).``Hidden Markov and Gaussian mixture models for automatic call classification" J.Acoust. Soc. Am. 125, EL221-224.


• Reconnaissance des instruments dans la musique polyphonique par décomposition NMF et classification SVM, Alexey Ozerov, Slim Essid and Maurice Charbit


• Incorporating prior knowledge on the digital media creation process into audio classifiers, M. Lardeur, S. Essid, G. Richard


• The Significance of the Non-Harmonic “Noise” Versus the Harmonic Series for Musical Instrument Recognition, Livshin, A., Rodet, X., 2006. ISMIR 2006


• Livshin, A., Rodet, X. 2009 - Purging Musical Instrument Sample Databases Using Automatic Musical Instrument Recognition Methods IEEE Transactions on Audio, Speech and Language Processing, Vol 17, no. 5. pp. 1046-1051, July 2009


• Martins, L.G. (PhD Thesis) 2009 - Livshin, A., Rodet, X. 2009 - Purging Musical Instrument Sample Databases Using Automatic Musical Instrument Recognition Methods


• Jiang, W., Zhang, X., Ras, Z.W., and Cohen, A. (2009). Multiple Classifiers for Different Features in Timbre Estimation, in Advances in Intelligent Information Systems (Eds. Ras, Z.W. and Tsay, L.), Studies in Computational Intelligence, Springer, will appear


• Soo-Chang Pei, Nien-Teh Hsu 2009 - Instrumentation analysis and identification of polyphonic music using beat-synchronous feature integration and fuzzy clustering

ISMIR 2009, Session Title: Musical Instrument Recognition & Multipitch Detection

• Fuhrmann, F., Haro, M., Herrera, P. (2009). Scalability, generality and temporal aspects in automatic recognition of predominant musical instruments in polyphonic music. Conference of the International Society for Music Information Retrieval (ISMIR)


• Musical Instrument Recognition in Polyphonic Audio Using Source-Filter Model for Sound Separation, Toni Heittola, Anssi Klapuri and Tuomas Virtanen (ISMIR)

ISMIR 2009 Poster Session (PS3):

• Automatic Identification of Instrument Classes in Polyphonic and Poly-Instrument Audio, Philippe Hamel, Sean Wood and Douglas Eck

Perceptions based on prior knowledge?

I found a very interesting journal article on how our perceptions are based on prior experience. It ties in with the perception of sounds.

Some more papers on instrument ID

Three papers my supervisor has brought to my attention:

• "Instrumentation analysis and identification of polyphonic music using beat-synchronous feature integration and fuzzy clustering", by Soo-Chang Pei and Nien-Teh Hsu
  
• "Polyphonic Musical Instrument Recognition Based on a Dynamic Model of the Spectral Envelope", J.J. Burred, A. Röbel and T. Sikora
• "Musical Instrument Recognition in Polyphonic Audio Using Source-Filter Model for Sound Separation", T. Heittola, A. Klapuri, and T. Virtanen

IPEM and the psychology of electronic music

I'm working with the IPEM Toolbox. A difficult task given that it's tied to Matlab versions 5.3.1 and 6.0. The source code is available and can be toyed with to try to make it compatible with later versions. A fix is to find the original versions, more as a time saving exercise! But as an aside, the concept of IPEM and the extraction of perceptual features is of interest to my own research. And in particular, being an electronic music fan, this article discusses the "Psychology of Electronic Music".

4475 separted notes

I separated the notes from the majority of the IOWA musical instrument samples. Total notes = 4475. WOW! It wasn't easy! I'll be writing about how I did it in my next publication. Look out a few short lines describing about 9 day's of hard work!!