Title :
Automatic Musical Instrument Recognition Using K-NN and MLP Neural Networks
Author :
Azarloo, Akram ; Farokhi, Fardad
Author_Institution :
Fac. of Electr. & Electron. Eng., Islamic Azad Univ., Tehran, Iran
Abstract :
In this paper, we proposed an approach to Musical Instrument Automatic Recognition. We used seven different musical instruments to be played simultaneously from solos to quartets. Our data have 296 feature vectors that used in audio signal classification by MLP neural networks and K-NN algorithm. Finally, MLP achieved as the best neural network in musical instrument recognition.
Keywords :
audio signal processing; multilayer perceptrons; musical instruments; signal classification; K-NN algorithm; MLP neural networks; audio signal classification; automatic musical instrument recognition; musical instrument automatic recognition; Accuracy; Classification algorithms; Feature extraction; Instruments; Mel frequency cepstral coefficient; Support vector machine classification; Training; Feature Extraction; K-Nearest Neighbors (K-NN); Multi Layer Perceptron (MLP); Musical Instrument Recognition; UTA;
Conference_Titel :
Computational Intelligence, Communication Systems and Networks (CICSyN), 2012 Fourth International Conference on
Conference_Location :
Phuket
Print_ISBN :
978-1-4673-2640-7
DOI :
10.1109/CICSyN.2012.61
