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Re: recognizing a source by its harmonic structure
It should be mentioned that most of these articles can be find on the net.
see:
Audio Research Group, Tampere University Of Technology, Finland –
http://www.c <http://www.cs.tut.fi/sgn/arg/>
s.tut.fi/sgn/arg/BM__Hlt24948962
Antti Eronen – Automatic musical instrument recognition, pattern and speech
recognition
http://www.cs. <http://www.cs.tut.fi/~eronen/> tut.fi/~eronen/
Departement TSI : [Traitement du Signal et des images / Signal and Imaes
Processing], Ecole Nationale Superieure des Telecommunications, Telecom
Paris,
France – <http://www.tsi.enst.fr/tsi/dep-tsi-eng.html>
http://www.tsi.enst.fr/tsi/dep-tsi-eng.html
Yves Grenier – Separation of musical sounds
<http://www.tsi.enst.fr/~grenier/> http://www.tsi.enst.fr/~grenier/
EMS : [Experimental Music Studios], University of Illinois, USA –
http://ems.music.ui <http://ems.music.uiuc.edu/> uc.edu/BM__Hlt24955182
James Beauchamp – Spectral Dynamic Synthesis, spectral centroid variation,
spectral envelope irregularity
http://ems.music.uiuc.e <http://ems.music.uiuc.edu/~beaucham/>
du/~beaucham/
MIT Media Lab, MIT, Cambridge, MA, USA – http://www.
<http://www.media.mit.edu/> media.mit.edu/BM__Hlt24959265BM__Hlt24959276
1. Hyperinstruments, MIT, USA - http://www.
<http://www.media.mit.edu/hyperins/> media.mit.edu/hyperins/BM__Hlt24959284
Tod Machover – Computer Music, Intelligent Music Instruments
http://web <http://web.media.mit.edu/~tod/>
.media.mit.edu/~tod/BM__Hlt24959318BM__Hlt24959327
Eric Métois – Media Sound Information http://xenia.media
<http://xenia.media.mit.edu/~metois/Phd/eymphd.pdf>
.mit.edu/~metois/Phd/eymphd.pdfBM__Hlt24959379BM__Hlt24959360
http://xenia <http://xenia.media.mit.edu/~metois/>
.media.mit.edu/~metois/BM__Hlt24959422
2. Music, Mind and Machine Group, MIT, USA -http://sound.m
<http://sound.media.mit.edu/index.html>
edia.mit.edu/index.htmlBM__Hlt24959521BM__Hlt18381860
Judith C. Brown – Audio Signal Processing
http://www.wellesl <http://www.wellesley.edu/Physics/brown/jbrown.html>
ey.edu/Physics/brown/jbrown.htmlBM__Hlt24959554
Barry Vercoe – Synthetic Listeners and Performers
http://web.m <http://web.media.mit.edu/~bv/>
edia.mit.edu/~bv/BM__Hlt24962851
Keith D. Martin - Sound-Source <http://xenia.media.mit.edu/~kdm/research>
Recognition: A Theory and Computational Model
http://xenia.media.mit.edu/~ <http://xenia.media.mit.edu/~kdm/research/>
kdm/research/BM__Hlt24962904
Eric Scheirer – MPEG-4, music-analysis systems, Synthetic listeners,
structured audio
http://web.media.mit.edu/~eds/ <http://web.media.mit.edu/~eds/> (inc.
thesis : Music-Listening Systems)
MMK : [Mensh-Maschine-Kommunikation], Technische Universität München,
Germany - http://www.mm
<http://www.mmk.e-technik.tu-muenchen.de/index_e.html>
k.e-technik.tu-muenchen.de/index_e.htmlBM__Hlt24962935
Ernst Terhardt – Perception of Auditory Pitch
http://www.mmk <http://www.mmk.e-technik.tu-muenchen.de/persons/ter.html>
.e-technik.tu-muenchen.de/persons/ter.html
MTG : [ Music Technology Group], Pompeu Fabra University, Barcelona, Spain
-http://w <http://www.iua.upf.es/mtg/eng/>
ww.iua.upf.es/mtg/eng/BM__Hlt18397275BM__Hlt24963139BM__Hlt24963399
Xavier Serra – Spectral processing
http://www.iua.up <http://www.iua.upf.es/~xserra/angles.html>
f.es/~xserra/angles.htmlBM__Hlt24963305BM__Hlt24963265BM__Hlt24963223BM__Hlt
24963207
Perfecto Herrera – Transmitting Audio Content as Sound Object
<http://www.iua.upf.es/~perfe/> http://www.iua.upf.es/~perfe/
Parmly Hearing Institute, Loyola University Chicago, USA –
http://www.parml <http://www.parmly.luc.edu/> y.luc.edu/BM__Hlt24965309
Gregory Sandell – Sharc database (http://www.
<http://www.auditory.org/postings/1994/206.html>
auditory.org/postings/1994/206.htmlBM__Hlt24965329BM__Hlt24965321 ;
http://sparky.ls.luc.edu/sandell/sharc/
<http://sparky.ls.luc.edu/sandell/sharc/README.html>
README.htmlBM__Hlt18823078 , http://spark <http://sparky.ls.luc.edu/sharc/>
y.ls.luc.edu/sharc/BM__Hlt24965353)
http://www.tedcrane.com/DanceDB/DisplayIdent.com?key=GREG_SANDELL
<http://www.tedcrane.com/DanceDB/DisplayIdent.com?key=GREG_SANDELL>
Peabody Institute, Baltimore, USA - http://www.pe
<http://www.peabody.jhu.edu/> abody.jhu.edu/BM__Hlt24965546
Ichiro Fujinaga – Optical music recognition, lazy learning (exemplar-based
learning),
digital signal processing, pattern recognition, music perception.
http://gigue.peabody.j <http://gigue.peabody.jhu.edu/~ich/>
hu.edu/~ich/BM__Hlt24965556BM__Hlt25480710BM__Hlt29891118BM__Hlt29884728BM__
Hlt24963004BM__Hlt24962989
BM__Hlt24955207
BM__Hlt24948985-----Original Message-----
From: Ladislava Janku [mailto:jankul@LAB.FELK.CVUT.CZ]
Sent: Fri 27/06/2003 09:28
To: AUDITORY@LISTS.MCGILL.CA
Cc:
Subject: Re: recognizing a source by its harmonic structure
Hi!
There is lot of work done in music instruments classification by frequency
spectrum, cepstrum or other approaches
For example, the following papers concern this problem:
Brown, J.C. (1999). ``Computer identification of musical instruments using
pattern recognition with cepstral coefficients as features'' J. Acoust. Soc.
Am. 105, 1933-1941.
Herrera P., Amatriain X., Batlle E., and Serra X. Towards Instrument
Segmentation for Music Content Description: a Critical Review of Instrument
Classification Techniques. In Proc. of International Symposium on Music
Information Retrieval, 2000.
Liu, Wan Feature selection for automatic classification of musical
instrument sounds Proceedings of the first ACM/IEEE-CS joint conference on
Digital libraries Roanoke, Virginia, United States Pages: Pages: 247 - 248
Year of Publication: 2001
ISBN:1-58113-345-6
Brown, J.C. (1996). "Frequency ratios of spectral components of musical
sounds" J. Acoust. Soc. Am. 99, 1210-1218.
Brown, J.C., Houix, O. & McAdams, S. (2001) Feature dependence in the
automatic identification of musical woodwind instruments. J. Acoust. Soc.
Am. 109, pp. 1064-1072.
Kinoshita, T., Sakai, S. & Tanaka, H. (1999) Musical soundsource
identification based on frequency component adaptation. Proc. IJCAI-99
Workshop on ComputationalAuditory Scene Analysis, Stockholm, Sweden.
Marques, J. & Moreno, P. (1999) A study of musical instrument classification
using Gaussian mixture models andsupport vector machines. Cambridge Research
LaboratoryTechnical Report Series CRL/4.
Martin, K. (1999) Sound-source recognition: A theory andcomputational model.
PhD Thesis, MIT
G.J. Brown, J. Egging A MISSING FEATURE APPROACH TO INSTRUMENT
IDENTIFICATION IN POLYPHONIC MUSIC, ICASSP03
Ladislava Janku
----- Original Message -----
From: "Gregoire, Jerry" <jgregoire@ECE.MONTANA.EDU>
To: <AUDITORY@LISTS.MCGILL.CA>
Sent: Thursday, June 26, 2003 10:33 PM
Subject: recognizing a source by its harmonic structure
> Does anyone know of work done that categorizes sources by patterns in
their
> harmonic structure.
>
> An example would be to separate a guitar from a flute using the harmonic
> relationships of f0, f1, f2, ... of a guitar compared to the flute's
> harmonics.
>
> Jerry Gregoire
>