NDLI: Feature selection based on MBFOA for audio signal classification under consideration of Gaussian white noise

Content Provider	IET Digital Library
Author	Arumugam, Muthumari Kaliappan, Mala
Abstract	Audio classification is a difficult task because of the issue of extracting and choosing the optimum audio features. To reduce the computational complication from existing methods, this study proposes a feature-selection method based on modified bacterial foraging optimisation algorithm (MBFOA) for classification of audio signals. Enhanced mel-frequency cepstral coefficient and enhanced power normalised cepstral coefficients with peak and pitch are estimated the signal feature and optimised using MBFOA with the fitness function. Using the probabilistic neural network, the audio signal is classified into music and speech signal. Then, if the signal is music, the signal is classified as cello, clarinet, flute etc. If the signal is detected as a speech, then it is again classified as male or female voice. This approach shows that it is possible to boost the classification accuracy by using different features and optimisation technique.
Starting Page	777
Ending Page	785
Page Count	9
ISSN	17519675
Volume Number	12
e-ISSN	17519683
Issue Number	Issue 6, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2016.0607
Journal	IET Signal Processing
Publisher Date	2018-03-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Audio Feature Extraction Audio Signal Classification Audio Signal Processing AWGN Computational Complication Reduction Enhanced Mel-frequency Cepstral Coefficient Enhanced Power Normalised Cepstral Coefficient Feature Selection Feature-selection Method Gaussian White Noise MBFOA Modified Bacterial Foraging Optimisation Algorithm Neural Computing Technique Neural Nets Optimisation Optimisation Technique Peak Estimation Pitch Estimation Probabilistic Neural Network Probability Signal Classification Signal Detection Signal Feature Estimation Speech And Audio Signal Processing Speech Processing Speech Processing Technique Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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