Re: recognizing a source by its harmonic structure ("F.R.Maintenant" )

Subject: Re: recognizing a source by its harmonic structure
From:    "F.R.Maintenant"  <F.R.Maintenant(at)OPEN.AC.UK>
Date:    Fri, 27 Jun 2003 18:48:37 +0100

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 <> Antti Eronen – Automatic musical instrument recognition, pattern and speech recognition http://www.cs. <> Departement TSI : [Traitement du Signal et des images / Signal and Imaes Processing], Ecole Nationale Superieure des Telecommunications, Telecom Paris, France – <> Yves Grenier – Separation of musical sounds <> EMS : [Experimental Music Studios], University of Illinois, USA – <> James Beauchamp – Spectral Dynamic Synthesis, spectral centroid variation, spectral envelope irregularity <> du/~beaucham/ MIT Media Lab, MIT, Cambridge, MA, USA – http://www. <> 1. Hyperinstruments, MIT, USA - http://www. <> Tod Machover – Computer Music, Intelligent Music Instruments http://web <> Eric Métois – Media Sound Information <> http://xenia <> 2. Music, Mind and Machine Group, MIT, USA -http://sound.m <> Judith C. Brown – Audio Signal Processing http://www.wellesl <> Barry Vercoe – Synthetic Listeners and Performers http://web.m <> Keith D. Martin - Sound-Source <> Recognition: A Theory and Computational Model <> kdm/research/BM__Hlt24962904 Eric Scheirer – MPEG-4, music-analysis systems, Synthetic listeners, structured audio <> (inc. thesis : Music-Listening Systems) MMK : [Mensh-Maschine-Kommunikation], Technische Universität München, Germany - <> Ernst Terhardt – Perception of Auditory Pitch http://www.mmk <> MTG : [ Music Technology Group], Pompeu Fabra University, Barcelona, Spain -http://w <> Xavier Serra – Spectral processing http://www.iua.up <> 24963207 Perfecto Herrera – Transmitting Audio Content as Sound Object <> Parmly Hearing Institute, Loyola University Chicago, USA – http://www.parml <> Gregory Sandell – Sharc database (http://www. <> ; <> README.htmlBM__Hlt18823078 , http://spark <> <> Peabody Institute, Baltimore, USA - <> Ichiro Fujinaga – Optical music recognition, lazy learning (exemplar-based learning), digital signal processing, pattern recognition, music perception. http://gigue.peabody.j <> Hlt24963004BM__Hlt24962989 BM__Hlt24955207 BM__Hlt24948985-----Original Message----- From: Ladislava Janku [mailto:jankul(at)LAB.FELK.CVUT.CZ] Sent: Fri 27/06/2003 09:28 To: AUDITORY(at)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(at)ECE.MONTANA.EDU> To: <AUDITORY(at)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 >

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