Gaussian Membership Function used for Voice Recognition in Fuzzy Logic
Shruti Agarwal1, Anshika Agarwal2, Prabhakar Gupta3
1Shruti Agarwal, Computer Science & Engineering, Shri Ram Murti Smarak College of Engeering & Technology, Bareilly, India.
2Anshika Agarwal, Computer Science & Engineering, Shri Ram Murti Smarak College of Engeering & Technology, Bareilly, India.
3Prabhakar Gupta, Computer Science & Engineering, Shri Ram Murti Smarak College of Engeering & Technology, Bareilly, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2685-2689 | Volume-8 Issue-5, January 2020. | Retrieval Number: F2543037619/2020©BEIESP | DOI: 10.35940/ijrte.F2543.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Abstract—Gaussian Membership function of a fuzzy set is a generalization form which is used to classify the human voice either based gender or age group. Membership functions were introduced by Zadeh in the first paper on fuzzy sets in the year 1965. In this paper we describe Gaussian membership function which we used to implement the simulation or classification of the human according to their age in fuzzy logic. A Gaussian Membership Function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1.
Keywords: Gaussian Membership Function, Voice Recognition, Maximum Frequency, Standard Deviation, Coefficient of variation.
Scope of the Article: Fuzzy logics.