Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment

Abstract

PurposeDue to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.Design/methodology/approachA novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.FindingsIt is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.Originality/valueThe generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

Research on optimization of case adaptation and enhancement of knowledge application benefits for multi-decision class cases based on FASS-NRS and SAGA-FCM

“…The k-nearest neighbor classification algorithm (KNN) is a supervised learning algorithm that takes into account both classification and regression problems and has the advantages of high accuracy and wide applicability; however, its noise immunity is weak, sensitive to noisy data, and the model is unstable and less repeatable (Maity et al, 2021). Therefore, this paper extends the KNN algorithm to the subspace KNN algorithm to improve the adaptation process's objectivity and enhance the adaptation model's stability.…”

“…The k-nearest neighbor classification algorithm (KNN) is a supervised learning algorithm that takes into account both classification and regression problems and has the advantages of high accuracy and wide applicability; however, its noise immunity is weak, sensitive to noisy data, and the model is unstable and less repeatable (Maity et al. , 2021).…”

Improved Expectation-Maximization Algorithm for Unknown Reverberant Audio-Source Separation

“…Many ICA-based algorithms have been applied in various fields, such as biomedical, audio, mechanical engineering [3]. In the underdetermined case, sparse component analysis (SCA) is a feasible method based on the assumption that only one source is active in each time-frequency slot, and has been an effective method in blind deconvolution [4][5].…”