Optimizing Self-Organizing Timbre Maps: Two Approaches
Music, Gestalt, and Computing - Studies in Cognitive and Systematic Musicology
Application of Temporal Descriptors to Musical Instrument Sound Recognition
Journal of Intelligent Information Systems
Estimation of musical sound separation algorithm effectiveness employing neural networks
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Analysis of Sound Features for Music Timbre Recognition
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Musical instrument timbres classification with spectral features
EURASIP Journal on Applied Signal Processing
Blind signal separation of similar pitches and instruments in a noisy polyphonic domain
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Hierarchical Tree for Dissemination of Polyphonic Noise
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Learning from Soft-Computing Methods on Abnormalities in Audio Data
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Identification of dominating instrument in mixes of sounds of the same pitch
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
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Research on automatic identification of musical instrument sounds has already been performed through last years, but mainly for monophonic singular sounds. In this paper we work on identification of musical instrument in polyphonic environment, with added accompanying orchestral sounds for the training purposes, and using mixes of two instrument sounds for testing. Four instruments of definite pitch has been used. For training purposes, these sounds were mixed with orchestral recordings of various levels, diminished with respect to the original recording level. The level of sounds added for testing purposes was also diminished with respect to the original recording level, in order to assure that the investigated instrument actually produced the sound dominating in the recording. The experiments have been performed using WEKA classification software.