Title :
Recognition of Musical Instruments
Author :
Wicaksana, Harya ; Hartono, Septian ; Wei, Foo Say
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Abstract :
In this paper an automated method to recognize the musical instruments playing the musical signals is presented. Various features of the musical instruments and musical signals are investigated. The features can broadly be grouped into three categories: temporal, spectral, and cepstral features. A composite neural network structure is proposed as the classifier. The performance of the composite neural network using a set of carefully chosen features is compared with that of the traditional neural network. Experimental results show that the accuracy achieved using composite structure (94%) is significantly higher than that using the traditional structure (88%) when more than four musical instruments are to be distinguished
Keywords :
acoustic signal processing; audio signal processing; feature extraction; musical instruments; neural nets; signal classification; automated method; cepstral features; composite neural network; musical instruments recognition; musical signals recognition; spectral features; temporal features; Cepstral analysis; Cepstrum; Character recognition; Feature extraction; Instruments; Mel frequency cepstral coefficient; Neural networks; Organizing; Signal processing; Timbre; musical instrument; neural network; recognition;
Conference_Titel :
Circuits and Systems, 2006. APCCAS 2006. IEEE Asia Pacific Conference on
Conference_Location :
Singapore
Print_ISBN :
1-4244-0387-1
DOI :
10.1109/APCCAS.2006.342417