Dual-tone Multifrequency Tones Research Articles

DTMF-based load control system

The aim of this paper is to create a remote-controlled home automation system using a mobile phone. One of the newest trends in home appliance control is home automation. Traditional wall switches are located throughout the home and must be physically pressed to turn ON or OFF loads. It is extremely difficult for the elderly or disabled. This technology is designed to enable mobile phone users to control home appliances by dialing a specific number. The advantage of the Dual-Tone Multi-Frequency (DTMF) system is that it can be controlled remotely from a distance, which reduces the burden on a person. Two mobile phones make up a DTMF based system, one phone serves as a remote control and the other as a receiver. Unlike conventional infrared and Radio Frequency (RF) controllers, it may be used to switch devices from anywhere. The device has an MT8870 DTMF decoder, which decodes DTMF tones. This proposed system gives a new direction to the development of home automation.

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 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.

Efficient Dual-tone Multi-frequency Signal Detection using a KNN Classifier

The requirement for an efficient method for noise-robust detection of Dual-tone Multi-frequency (DTMF) signals keeping in mind the continuous evolution of telecommunication equipment is conspicuous. A machine learning based approach has been proposed in this research article to detect DTMF tones under the influence of various noises and frequency variations by employing the K-Nearest Neighbor (KNN) Algorithm. In order to meet accurate classification/detection requirements for various real-world requirements, a total of four KNN models have been created and compared, and the best one proposed for real-time deployment. Two datasets have been amassed, a clean dataset without noise and a noisy augmented dataset with perturbations that are observed in telecommunication channels such as additive white gaussian noise (AWGN), amplitude attenuation, time shift/stretch etc. Mel-Frequency Cepstral Coefficients (MFCC) and Goertzel’s Algorithm (used to estimate the absolute Discrete Fourier Transform (DFT) values for the fundamental DTMF frequencies) are employed to calculate features to be fed to the KNN models. The four models differ in being trained with and without the augmented data using the two aforementioned feature extraction algorithms, namely MFCCs calculation and the Goertzel’s algorithm. The proposed models have been verified and validated with unseen noisy testing data and it was found that the proposed KNN model D outperformed all the other models with a macro recall, precision and F1 classification score of 97.7, 97.70625 and 97.70046 respectively. The proposed model is also computationally inexpensive and showcases relatively low computing time and complexity.

Efficient Automation using DTMF

We have seen the implementation of Home Automation using bluetooth and WIFI technologies, but the drawbacks of those technologies are range issues. The range upto where the devices can be connected is limited. In order to overcome that we propose a uniques System for automation using Dual Tone Multi Frequency (DTMF). This paper suggests an idea for controlling the devices using DTMF tone generated when the user presses the keypad buttons . The advantage of this system is that it can be operated from any distant or remote area.

Remote Controlling of Light Intensity Using Phone Devices

In this study, we investigate the design and simulation of an electronic system for remote controlling the level of light intensity of an electrical lighting system used for a certain desired application. The remote controlling is done using the keypad of any Dual Tone Multi Frequency (DTMF) based telephone handset. A DTMF tone command sent from a transmitting fixed or mobile phone terminal will be used to activate a system of relays that control the required level of light intensity. An appropriate active bandpass filters are designed for recognition of the DTMF tones of the transmitted digit. The bandpass filters and the additional subsequent stages of the overall electronic system are simulated and tested using MULTISM. Light intensity controlling is useful to avoid unnecessary electrical power wastage and the remote controlling eliminates the physical presence of a person for adjusting the required level of lighting at the desired location.

GSM based Wireless Sensor Network to Measure Global Warming, Humidity and CO2

This paper discusses GSM based global wireless sensor network to measure increase of global warming & air pollution. Each motes of the wireless sensor network can cover the area wherever GSM network is available. Each mote consists of temperature, humidity & co2 measuring sensors, equipped with low power consuming GSM module, data logging facility & real time stamp. Motes can communicate with each other but most of the time they communicate with the central receiver. Central receiver is capable of receiving calls from modes, display the data in portable TFT LCD screen & also transmit data to computer through USB. To transmit data sensors generate call to receive station. To receive data receiver first verify the caller ID and then it receives the call. Sensors send or receive data through Dual tone multi frequency (DTMF) and will receive commands through DTMF also. Lots of control system has been designed using DTMF but in this project DTMF tones are used to create all characters of an ASCII table. The number of motes can be connected are unlimited and the coverage area is also unlimited in this project. General Terms Wireless Sensor Network, GSM, DTMF, Sensors.