Safdar Tanweer ¹, Abdul Mobin ¹, Afshar Alam ¹

Affiliations
1 Jamia Hamdard (Hamdard University, Deemed to be University), Hamdard Nagar, New Delhi - 110062, Delhi, IN

Abstract

Objective: The impact of feature based environmental noise classification and their efficiencies are discussed in this paper. The aim is to recognize, compare, classifyandidentify with the help of computation and applied techniques. Methods/Statistical Analysis: To perform the task of classification of noise sources, LPC and MFCC were used as input to the classifiers in experimental work. LDA, QDA and ANN are tested for the classification purpose. Findings: Once the source is identified we can address these untoward noisiness class and to minimize their impact to the human perceptions by some means or in speech recognition task to enhance the system recognition efficiency. The source can be identified by analysis of various categories of classifier in association with specific feature of noise source.LDA used with LPC gives an overall efficiency of classification is about 65.1% and with MFCC it is about 77.9%. QDA used with LPC gives an overall efficiency of 72.7% and with MFCC it is about 86.3%. ANN with LPC gives an overall efficiency of classification 83.2% and with MFCC it is about 90%. The MSE’s (mean squared error) of ANN with MFCC are found to be 4.94838-e^-2 (training), 5.33561-e^-2 (validation) and 6.95805-e^-2 (testing) and the %error for the same are 9.40265 (training), 10.02949 (validation) and 14.45427 (testing). Application/improvement: The performance of LDA, QDA and ANN with LPC and MFCC is analyzed. It is evident that ANN in combination with MFCC gives the best result and showing efficiency about 90%.

Keywords

ANN, Classifier, Environmental Noise, LDA, LPC, MFCC, QDA.

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Jawed Ahmed ¹, M. Afshar Alam ¹, Abdul Mobin ²

Affiliations
1 Department of Computer Science, JamiaHamdard (Hamdard University), New Delhi – 110062, IN
2 Department of CSIR-National Physical Laboratory, Dr. K. S. Krishnan Marg, New Delhi 110012, IN

Abstract

Objectives: In this paper, we have justified that existing notion of Body Mass Index (BMI) does not signify the body fat mass with a good precision. So a new version of BMI called BMI-2 is proposed. Method: The body has some parts where chance of depositing the body fat is high i.e., waist, hip, thigh and neck etc. Here, in this paper, we have multiplied the circumferences of waist, hip, thigh and neck with the existing formula of BMI. Findings: A person with high circumferences of waist, hip, thigh and neck would be having comparatively higher value of new BMI-2 than that of others; even their old (existing) BMI values are same. Improvement: The new BMI-2 gives a comparatively better picture of health status by including the circumferences of some fat prone parts of the body. But if we had taken the weighted circumferences, the result would have been more accurate.

Keywords

BMI, FC Index, WC, WHR, Weighted Circumference.

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