Automatic musical instrument classification using fractional fourier transform based- MFCC features and counter propagation neural network
| Autor: | D. G. Bhalke, C. B. Rao, Dattatraya S. Bormane |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Musical instrument classification
Computer Networks and Communications Computer science Speech recognition Feature extraction 02 engineering and technology 030507 speech-language pathology & audiology 03 medical and health sciences symbols.namesake Artificial Intelligence Robustness (computer science) 0202 electrical engineering electronic engineering information engineering business.industry Pattern recognition Fractional Fourier transform ComputingMethodologies_PATTERNRECOGNITION Additive white Gaussian noise Computer Science::Sound Hardware and Architecture symbols 020201 artificial intelligence & image processing Mel-frequency cepstrum Artificial intelligence 0305 other medical science business Classifier (UML) Timbre Software Information Systems |
| Zdroj: | Journal of Intelligent Information Systems. 46:425-446 |
| ISSN: | 1573-7675 0925-9902 |
| DOI: | 10.1007/s10844-015-0360-9 |
| Popis: | This paper presents a novel feature extraction scheme for automatic classification of musical instruments using Fractional Fourier Transform (FrFT)-based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using Counter Propagation Neural Network (CPNN). The discriminating capability of the proposed features have been maximized for between-class instruments and minimized for within-class instruments compared to other conventional features. Also, the proposed features show significant improvement in classification accuracy and robustness against Additive White Gaussian Noise (AWGN) compared to other conventional features. McGill University Master Sample (MUMS) sound database has been used to test the performance of the system. |
| Databáze: | OpenAIRE |
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