Publication: Intelligent detection of DTMF tones using a hybrid signal processing technique with support vector machines
Date
2008
Authors
Nagi J.
Yap K.S.
Tiong S.K.
Ahmed S.K.
Nagi F.
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Abstract
Efficient methods for DTMF signal detection are important for developing telecommunication equipment. This paper presents a hybrid signal processing and artificial intelligence based approach for the detection of Dual-tone Multifrequency (DTMF) tones under the influence of White Gaussian Noise (WGN) and frequency variation. Key innovations include the use of a Finite Impulse Response (FIR) bandpass filter for reduction of noise from DTMF input samples, and Support Vector Machines (SVM) for intelligent classification of the detected DTMF carrier frequencies. The proposed hybrid DTMF detector scheme is based on power spectrum analysis by means of the Discrete Fourier Transform (DFT). The Goertzel's Algorithm is used to estimate the seven fundamental DTMF carrier frequencies. The tone detection scheme employs decision logic to detect valid DTMF tones from low and high DTMF frequency groups. Comparison of this hybrid DTMF tone detection model with existing DTMF detection techniques proves the merits of this proposed scheme. � 2008 IEEE.
Description
Keywords
Artificial intelligence , Bandpass filters , Civil aviation , Discrete Fourier transforms , Fourier transforms , Image retrieval , Impulse response , Information technology , Power spectrum , Signal processing , Spectrum analysis , Spectrum analyzers , Support vector machines , Telecommunication , Telecommunication equipment , Vectors , Carrier frequencies , Decision logics , Detection models , Detection schemes , Detection techniques , Dtmf frequencies , Efficient methods , Finite impulse responses , Frequency variations , Hybrid signal processing , Input samples , Intelligent classifications , Intelligent detections , Multi frequencies , Power Spectrum analysis , Support vectors , White gaussian noises , Frequency response