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!!

Another Instrument ID paper

I came across another paper on musical instrument ID today:

Loughran,R., Walker,J., O'Neill, M., O'Farrell, M., 2008 - Musical Instrument Identification Using Principal Component Analysis and Multi-Layered Perceptrons.pdf

Loughran, R. uses the temporal envelope of the signal residual (post removal of the RMS temporal envelope) as a feature which I haven't come across before in the literature.

"Temporal and spectral envelopes. The temporal envelope was found by calculating the RMS energy envelope of each sound, which was then filtered using a 3rd order low pass Butterworth filter. This envelope was calculated over the length of each note and so includes temporal information on how the energy within the sound changes over time. Thus this envelope incorporates information regarding the attack time which has been shown to be of high importance to instrument classification [13]. The temporal envelope was then subtracted from the original sound to find the residual. The temporal residual envelope was calculated from the RMS of this residual.

A possible approach to data preparation

As more of a note to self, there are some interesting aspects of the final year project, "Musical Instrument Detection" by Gautham J. Mysore and Gregory Sell and SongHui Chon, which I may consider:

1. The ensemble approach to classification:
   "...we attempt the problem of identifying the instrumentation of a musical signal at any given time using several machine learning techniques(logistic regression, K-NN, SVM).We approached the problem as a series of separate binary classifications (as opposed to a multivariate problem) so that we could mix and match the best algorithm for each instrument to create the best overall classifier."
2. The mixture signals were created artificially:
   "Then, to create one of the combinations above, a random signal for each instrument were all combined randomly. In this way, we created 52 total signals for each instrumental combination."

HMM toolboxes

Some toolboxes and links:

Hidden Markov Model Toolbox for Matlab
Mendel HMM Toolbox for Matlab
H2M: A set of Matlab/Octave functions for the estimation of mixtures and hidden markov models