Earticle

초록

영어

This paper presents musical instrument recognition for isolated music sound signals using hybridization of fractional fourier transform (FRFT) based features with timbrel (acoustic) features using feed forward neural network. The FRFT based features which is named as fractional MFCC are computed by replacing conventional discrete fourier transform in mel frequency cepstral coefficient (MFCC) with discrete FRFT. Hybrid features are obtained by effectively combining Fractional MFCC with timbrel features such as temporal, spectral and cepstral features. Feed forward neural network with back propagation algorithm has been used to test the performance of system and results were compared in terms of recognition accuracy and number of features. Proposed feature out performs over individual and other traditional features proposed in the literature. The experimentation is performed on isolated musical sounds of 19 musical instruments covering four different instrument families. The system is tested on benchmarked McGill University musical sound database.

목차

Abstract
 1. Introduction
 2. Proposed System
 3. Feature Extraction
  3.1. Mel Frequency Cepstral Coefficient (MFCC):
  3.2 Fractional Fourier Transform based MFCC (Fractional MFCC):
  3.3. Timbrel Features:
  3.4. Spectral Features:
  3.5. Temporal Features:
  3.6. Proposed Hybrid Features:
 4. Database Details
 5. Performance Analysis
 6. Conclusion
 References

키워드

저자

• D. G. Bhalke [ National Institute of Technology, Waranga, India ]
• C. B. Rama Rao [ National Institute of Technology, Waranga, India ]
• D. S. Bormane [ JSPM’s RSCOE, Pune, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605