Publication:
Dual-tone multifrequency signal detection using support vector machines

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dc.citedby5
dc.contributor.authorNagi J.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorAhmed S.K.en_US
dc.contributor.authorid25825455100en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid25926812900en_US
dc.date.accessioned2023-12-29T07:57:24Z
dc.date.available2023-12-29T07:57:24Z
dc.date.issued2008
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4814301
dc.identifier.doi10.1109/NCTT.2008.4814301
dc.identifier.epage355
dc.identifier.scopus2-s2.0-67650162423
dc.identifier.spage350
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-67650162423&doi=10.1109%2fNCTT.2008.4814301&partnerID=40&md5=b0af4007ba06dd38598a6bc5f6b897d7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30991
dc.pagecount5
dc.sourceScopus
dc.sourcetitleProceedings of IEEE 2008 6th National Conference on Telecommunication Technologies and IEEE 2008 2nd Malaysia Conference on Photonics, NCTT-MCP 2008
dc.subjectDiscrete fourier transform
dc.subjectDual-tone multifrequency tone
dc.subjectGoertzel's algorithm
dc.subjectSupport vector machine
dc.subjectArtificial intelligence
dc.subjectBlock codes
dc.subjectDiscrete Fourier transforms
dc.subjectExpert systems
dc.subjectGears
dc.subjectGroup technology
dc.subjectImage retrieval
dc.subjectImage storage tubes
dc.subjectMATLAB
dc.subjectMultilayer neural networks
dc.subjectPower spectrum
dc.subjectSignal detection
dc.subjectSignal processing
dc.subjectSpectrum analysis
dc.subjectSpectrum analyzers
dc.subjectTelecommunication
dc.subjectTelecommunication equipment
dc.subjectVectors
dc.subjectCarrier frequency
dc.subjectDecision logic
dc.subjectDetection models
dc.subjectDetection scheme
dc.subjectDetection technique
dc.subjectDTMF frequencies
dc.subjectDual-tone multifrequency tone
dc.subjectEfficient detection
dc.subjectFrequency variation
dc.subjectGoertzel's algorithm
dc.subjectInput sample
dc.subjectMATLAB environment
dc.subjectMulti frequency
dc.subjectMultifrequency signals
dc.subjectPerformance tests
dc.subjectRule base
dc.subjectSoftware-based
dc.subjectSVM classifiers
dc.subjectWhite Gaussian noise
dc.subjectSupport vector machines
dc.titleDual-tone multifrequency signal detection using support vector machinesen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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