Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment
Purpose – Due 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/approach – A novel machine learning-based approach to detect DTMF tones aff...
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Emerald Publishing
2025-01-01
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| Series: | Applied Computing and Informatics |
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| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/ACI-10-2020-0105/full/pdf |
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| author | Arunit Maity P. Prakasam Sarthak Bhargava |
| author_facet | Arunit Maity P. Prakasam Sarthak Bhargava |
| author_sort | Arunit Maity |
| collection | DOAJ |
| description | Purpose – Due 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/approach – A 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. Findings – It 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/value – The 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. |
| format | Article |
| id | doaj-art-20b63bb585d54bcb864ba9463af448b6 |
| institution | Kabale University |
| issn | 2634-1964 2210-8327 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Applied Computing and Informatics |
| spelling | doaj-art-20b63bb585d54bcb864ba9463af448b62025-01-28T12:19:18ZengEmerald PublishingApplied Computing and Informatics2634-19642210-83272025-01-01211/2122310.1108/ACI-10-2020-0105Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environmentArunit Maity0P. Prakasam1Sarthak Bhargava2School of Electronics Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Vellore, IndiaPurpose – Due 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/approach – A 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. Findings – It 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/value – The 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.https://www.emerald.com/insight/content/doi/10.1108/ACI-10-2020-0105/full/pdfDual-tone multi-frequencyKNN classifierDFT coefficientsGoertzel's algorithm |
| spellingShingle | Arunit Maity P. Prakasam Sarthak Bhargava Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment Applied Computing and Informatics Dual-tone multi-frequency KNN classifier DFT coefficients Goertzel's algorithm |
| title | Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment |
| title_full | Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment |
| title_fullStr | Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment |
| title_full_unstemmed | Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment |
| title_short | Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment |
| title_sort | robust dual tone multi frequency tone detection using k nearest neighbour classifier for a noisy environment |
| topic | Dual-tone multi-frequency KNN classifier DFT coefficients Goertzel's algorithm |
| url | https://www.emerald.com/insight/content/doi/10.1108/ACI-10-2020-0105/full/pdf |
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