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

fYear

2012

fDate

24-26 July 2012

Firstpage

289

Lastpage

294

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence, Communication Systems and Networks (CICSyN), 2012 Fourth International Conference on

Conference_Location

Phuket

Print_ISBN

978-1-4673-2640-7

Type

conf

DOI

10.1109/CICSyN.2012.61

Filename

6274357

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3057606