Cover Image

Dual-tone multifrequency signal detection using support vector machines

The need for efficient detection of Dual-tone Multifrequency (DTMF) tones for developing telecommunication equipment is justifiable. This paper presents an artificial intelligence based approach for efficient detection of DTMF tones under the influence of White Gaussian Noise (WGN) and frequency var...

Full description

Saved in:
Bibliographic Details
Main Authors: Nagi, J., Tiong, S.K., Yap, K.S., Ahmed, S.K.
Format: Conference Paper
Language:English
Published: 2017
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-5038
record_format dspace
spelling my.uniten.dspace-50382017-11-14T08:01:21Z Dual-tone multifrequency signal detection using support vector machines Nagi, J. Tiong, S.K. Yap, K.S. Ahmed, S.K. The need for efficient detection of Dual-tone Multifrequency (DTMF) tones for developing telecommunication equipment is justifiable. This paper presents an artificial intelligence based approach for efficient detection of DTMF tones under the influence of White Gaussian Noise (WGN) and frequency variation, using Support Vector Machines (SVM). Additive WGN in the DTMF input samples is removed by filtering out unwanted frequencies. Detection of DTMF carrier frequencies from input samples employs a traditional software based approach using the power spectrum analysis of the Discrete Fourier Transform (DFT) signals. The Goertzel's Algorithm is used to estimate the seven fundamental DTMF carrier frequencies. A SVM classifier is trained using the estimated fundamental DTMF carrier frequencies, and is validated using the input samples for classification of low and high DTMF frequency groups. The tone detection scheme employs decision logic using a rule-base expert system for classification of low and high DTMF frequency groups, corresponding to valid DTMF frequency groups. Comparison of this hybrid DTMF tone detection model with existing DTMF detection techniques proves the merits of this proposed scheme. This hybrid DTMF tone detection scheme is simulated in a MATLAB environment and results from performance tests are given in this paper. © 2008 IEEE. 2017-11-14T03:21:34Z 2017-11-14T03:21:34Z 2008 Conference Paper 10.1109/NCTT.2008.4814301 en Proceedings of IEEE 2008 6th National Conference on Telecommunication Technologies and IEEE 2008 2nd Malaysia Conference on Photonics, NCTT-MCP 2008 2008, Article number 4814301, Pages 350-355
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description The need for efficient detection of Dual-tone Multifrequency (DTMF) tones for developing telecommunication equipment is justifiable. This paper presents an artificial intelligence based approach for efficient detection of DTMF tones under the influence of White Gaussian Noise (WGN) and frequency variation, using Support Vector Machines (SVM). Additive WGN in the DTMF input samples is removed by filtering out unwanted frequencies. Detection of DTMF carrier frequencies from input samples employs a traditional software based approach using the power spectrum analysis of the Discrete Fourier Transform (DFT) signals. The Goertzel's Algorithm is used to estimate the seven fundamental DTMF carrier frequencies. A SVM classifier is trained using the estimated fundamental DTMF carrier frequencies, and is validated using the input samples for classification of low and high DTMF frequency groups. The tone detection scheme employs decision logic using a rule-base expert system for classification of low and high DTMF frequency groups, corresponding to valid DTMF frequency groups. Comparison of this hybrid DTMF tone detection model with existing DTMF detection techniques proves the merits of this proposed scheme. This hybrid DTMF tone detection scheme is simulated in a MATLAB environment and results from performance tests are given in this paper. © 2008 IEEE.
format Conference Paper
author Nagi, J.
Tiong, S.K.
Yap, K.S.
Ahmed, S.K.
spellingShingle Nagi, J.
Tiong, S.K.
Yap, K.S.
Ahmed, S.K.
Dual-tone multifrequency signal detection using support vector machines
author_facet Nagi, J.
Tiong, S.K.
Yap, K.S.
Ahmed, S.K.
author_sort Nagi, J.
title Dual-tone multifrequency signal detection using support vector machines
title_short Dual-tone multifrequency signal detection using support vector machines
title_full Dual-tone multifrequency signal detection using support vector machines
title_fullStr Dual-tone multifrequency signal detection using support vector machines
title_full_unstemmed Dual-tone multifrequency signal detection using support vector machines
title_sort dual-tone multifrequency signal detection using support vector machines
publishDate 2017
_version_ 1644493597263790080
score 13.222552

Similar Items