Towards Automatic Musical Instrument Timbre Recognition

This book is comprised of two parts topics concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction, and a Radial/Elliptical Basis Function neural network-based pattern recognition module. Significant emphasis has been put on feature extraction development for robust and consistent feature vectors for pattern recognition. The compositional part of the essay includes brief introductions to A d Ess Are, Aboji, 48 13 N, 16 20 O, and pH-SQ.

Autorentext

Tae Hong Park, B.Eng., M.A., M.F.A, Ph.D. degrees from Korea University, Dartmouth College, and Princeton University. Associate Researcher, LG Central Research Laboratory Seoul, Korea. Currently Assistant Professor and Head, Music Science and Technology Program, Tulane University.

Klappentext

This book is comprised of two parts - topics concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction, and a Radial/Elliptical Basis Function neural network-based pattern recognition module. Significant emphasis has been put on feature extraction development for robust and consistent feature vectors for pattern recognition. The compositional part of the essay includes brief introductions to "A d'Ess Are," "Aboji," "48 13 N, 16 20 O," and "pH-SQ."