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...
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my.uniten.dspace-309912023-12-29T15:57:24Z Dual-tone multifrequency signal detection using support vector machines Nagi J. Tiong S.K. Yap K.S. Ahmed S.K. 25825455100 15128307800 24448864400 25926812900 Discrete fourier transform Dual-tone multifrequency tone Goertzel's algorithm Support vector machine Artificial intelligence Block codes Discrete Fourier transforms Expert systems Gears Group technology Image retrieval Image storage tubes MATLAB Multilayer neural networks Power spectrum Signal detection Signal processing Spectrum analysis Spectrum analyzers Telecommunication Telecommunication equipment Vectors Carrier frequency Decision logic Detection models Detection scheme Detection technique DTMF frequencies Dual-tone multifrequency tone Efficient detection Frequency variation Goertzel's algorithm Input sample MATLAB environment Multi frequency Multifrequency signals Performance tests Rule base Software-based SVM classifiers White Gaussian noise 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 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. Final 2023-12-29T07:57:24Z 2023-12-29T07:57:24Z 2008 Conference paper 10.1109/NCTT.2008.4814301 2-s2.0-67650162423 https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650162423&doi=10.1109%2fNCTT.2008.4814301&partnerID=40&md5=b0af4007ba06dd38598a6bc5f6b897d7 https://irepository.uniten.edu.my/handle/123456789/30991 4814301 350 355 Scopus |
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Discrete fourier transform Dual-tone multifrequency tone Goertzel's algorithm Support vector machine Artificial intelligence Block codes Discrete Fourier transforms Expert systems Gears Group technology Image retrieval Image storage tubes MATLAB Multilayer neural networks Power spectrum Signal detection Signal processing Spectrum analysis Spectrum analyzers Telecommunication Telecommunication equipment Vectors Carrier frequency Decision logic Detection models Detection scheme Detection technique DTMF frequencies Dual-tone multifrequency tone Efficient detection Frequency variation Goertzel's algorithm Input sample MATLAB environment Multi frequency Multifrequency signals Performance tests Rule base Software-based SVM classifiers White Gaussian noise Support vector machines |
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Discrete fourier transform Dual-tone multifrequency tone Goertzel's algorithm Support vector machine Artificial intelligence Block codes Discrete Fourier transforms Expert systems Gears Group technology Image retrieval Image storage tubes MATLAB Multilayer neural networks Power spectrum Signal detection Signal processing Spectrum analysis Spectrum analyzers Telecommunication Telecommunication equipment Vectors Carrier frequency Decision logic Detection models Detection scheme Detection technique DTMF frequencies Dual-tone multifrequency tone Efficient detection Frequency variation Goertzel's algorithm Input sample MATLAB environment Multi frequency Multifrequency signals Performance tests Rule base Software-based SVM classifiers White Gaussian noise Support vector machines Nagi J. Tiong S.K. Yap K.S. Ahmed S.K. Dual-tone multifrequency signal detection using support vector machines |
| 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. |
| author2 |
25825455100 |
| author_facet |
25825455100 Nagi J. Tiong S.K. Yap K.S. Ahmed S.K. |
| format |
Conference paper |
| author |
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 |
2023 |
| _version_ |
1806426146897657856 |
| score |
13.222552 |