Efficient Dual-tone Multi-frequency Signal Detection using a KNN Classifier

Authors

  • Arunit Maity  School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • Sarthak Bhargava  School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  • Prakasam P  School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

DOI:

https://doi.org//10.32628/IJSRST207543

Keywords:

Dual-tone multifrequency, K-nearest neighbors, Mel-Frequency Cepstral Coefficients, Goertzel's algorithm, machine learning

Abstract

The requirement for an efficient method for noise-robust detection of Dual-tone Multi-frequency (DTMF) signals keeping in mind the continuous evolution of telecommunication equipment is conspicuous. A machine learning based approach has been proposed in this research article to detect DTMF tones under the influence of various noises and frequency variations by employing the K-Nearest Neighbor (KNN) Algorithm. In order to meet accurate classification/detection requirements for various real-world requirements, a total of four KNN models have been created and compared, and the best one proposed for real-time deployment. Two datasets have been amassed, a clean dataset without noise and a noisy augmented dataset with perturbations that are observed in telecommunication channels such as additive white gaussian noise (AWGN), amplitude attenuation, time shift/stretch etc. Mel-Frequency Cepstral Coefficients (MFCC) and Goertzel

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International Journal of Scientific Research in Science and Technology

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Published

2020-10-30

Issue

Volume 7, Issue 5, September-October-2020

Section

Research Articles

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How to Cite

[1]
Arunit Maity, Sarthak Bhargava, Prakasam P, " Efficient Dual-tone Multi-frequency Signal Detection using a KNN Classifier, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 7, Issue 5, pp.208-224, September-October-2020. Available at doi : https://doi.org/10.32628/IJSRST207543