Fractional Fourier Transform Based Features for Musical Instrument Recognition Using Machine Learning Techniques

This paper reports the result of Musical instrument recognition using fractional fourier transform (FRFT) based features. The FRFT features are computed by replacing conventional Fourier transform in Mel Frequecny Cepstral coefficient (MFCC) with FRFT. The result of the system using FRFT is compared with the result of the system using Mel Frequency Cepstral Coefficients (MFCC), Wavelet and Timbrel features with different machine learning algorithms. The experimentation is performed on isolated musical sounds of 19 musical instruments covering four different instrument families. The system using FRFT features outperforms over MFCC, Wavelet and Timbrel features with 91.84% recognition accuracy for individual instruments. The system is tested on benchmarked McGill University musical sound database. The experimental result shows that musical sound signals can be better represented using FRFT.