Title :
Timbre recognition of single notes using an ARTMAP neural network
Author :
Fragoulis, D.K. ; Avaritsiotis, J.N. ; Papaodysseus, C.N.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Greece
Abstract :
In this paper, a model for the perception of musical instrument timbre is presented. The model uses an ARTMAP neural network to distinguish single notes played by five different instruments. The duration of each note is quite short. The recognition of timbre is based on three acoustic properties: spectral synchrony, slope of the attacks and spectral distribution. Arrays of values of the above properties are used as input patterns. By training the network with a large number of different input patterns a robust pattern recognizer for timbre identification is constructed. The choice of this specific type of neural network model provides the ability for creating timbre categories which can continuously be updated at any point of operation, while at the same time, knowledge of previously learned categories is retained
Keywords :
ART neural nets; musical acoustics; musical instruments; pattern recognition; ARTMAP neural network; duration; input patterns; musical instrument; robust pattern recognizer; single notes; slope; spectral distribution; spectral synchrony; timbre recognition; Acoustic measurements; Frequency; Instruments; Neural networks; Pattern recognition; Production; Robustness; Spectral shape; Statistics; Timbre;
Conference_Titel :
Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on
Conference_Location :
Pafos
Print_ISBN :
0-7803-5682-9
DOI :
10.1109/ICECS.1999.813404