Sistem Kontrol Perangkat Inframerah Menggunakan Speech Recognition dengan Spectrogram dan Convolutional Neural Network Berbasis Mikrokontroler
DOI:
https://doi.org/10.25126/jtiik.20231056909Abstrak
Menurut data dari Biro Pusat Statistik (BPS), terdapat sebanyak 22,5 juta dari penduduk Indonesia merupakan penyandang disabilitas. Angka ini berjumlah sekitar lima persen dari keseluruhan penduduk Indonesia. Di zaman sekarang, kemajuan teknologi di seluruh dunia berkembang dengan pesat, sehingga muncul banyak hal yang dapat membantu menyederhanakan kehidupan semua orang, terutama penyandang disabilitas. Salah satu hal yang membantu penyandang disabilitas adalah munculnya perangkat pintar yang dapat dikendalikan menggunakan indra selain tangan, seperti suara. Penelitian ini bertujuan untuk mengembangkan sistem yang dapat mengendalikan perangkat inframerah dengan menggunakan suara sebagai input. Sistem tersebut akan dikembangkan menggunakan mikrokontroler dan metode speech recognition yang terdiri dari spectrogram dan CNN. Penelitian ini direncanakan untuk tujuan untuk membantu penyandang disabilitas dalam mengendalikan perangkat-perangkat di sekitar rumah. Hasil pengujian menunjukkan bahwa akurasi model CNN sebesar 93% dan akurasi percobaan terhadap pengguna sebesar 74,25%. Sistem ini juga dapat menjalankan proses speech recognition dengan waktu rata-rata 0,105 detik. Jarak optimal yang diperlukan antara pengguna dengan mikrofon adalah 30 cm dan jarak optimal yang diperlukan antara transmitter inframerah dengan perangkat yang dikendalikan adalah 30 cm.
Abstract
According to data from the Central Bureau of Statistics (BPS), around 22.5 million of Indonesia's population are people with disabilities. This number amounts to about five percent of Indonesia's total population. In the present day, where technology advances are rapidly developing all around the world, there have been many things that can help simplify the lives of everyone in the world, especially people with disabilities. One thing that helps people with disabilities is the emergence of smart devices that do not need to be controlled using hands but can use other senses such as sound. This research aims to develop a system that can control infrared devices using sound as input. The system will be developed using microcontrollers and speech recognition methods consisting of spectrogram and CNN. This research is conducted with the goal of helping people with disabilities in controlling devices around the house. Testing results show that the accuracy of the CNN model is 93% and the accuracy of trials on users is 74.25%. The system can also run the speech recognition process with an average time of 0.105 seconds. The optimal distance required between the user and microphone is 30 cm and the optimal distance required between the infrared transmitter and the controlled device is 30 cm.
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ABDEL-HAMID, O., MOHAMED, A. R., JIANG, H., DENG, L., PENN, G., & YU, D., 2014. Convolutional neural networks for speech recognition. IEEE Transactions on Audio, Speech and Language Processing, 22(10), 1533–1545.
ABDUL QAYYUM, A.B., AREFEEN, A. AND SHAHNAZ, C., 2019. Convolutional Neural Network (CNN) based speech-emotion recognition. 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON), pp.122–125.
AJAEGBU, C., ADETUNJI, O., NWAOCHA, N., JULIANA, N., 2020. A Speech Activated Control System for Infrared Appliances. International Information and Engineering Technology Association (IIETA), 53:1, pp. 103-110.
AOUANI, H., & BEN AYED, Y., 2018. Emotion recognition in speech using MFCC with SVM, DSVM and auto-encoder. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 1–5.
BAJPAI, S., & RADHA, D., 2019. Smart Phone as a Controlling Device for Smart Home using Speech Recognition. 2019 International Conference on Communication and Signal Processing (ICCSP), 0701–0705. https://doi.org/10.1109/ICCSP.2019.8697923
BADSHAH, A.M., AHMAD, J., RAHIM, N. AND BAIK, S.W., 2017. Speech emotion recognition from spectrograms with deep convolutional neural network. 2017 International Conference on Platform Technology and Service (PlatCon), pp.1–5.
CURILEM, M., CANARIO, J. P., FRANCO, L., & RIOS, R. A., 2018. Using CNN To Classify Spectrograms of Seismic Events From Llaima Volcano (Chile). 2018 International Joint Conference on Neural Networks (IJCNN), 1–8.
KHAN, A. S., AHMAD, Z., ABDULLAH, J., & AHMAD, F., 2021. A Spectrogram Image-Based Network Anomaly Detection System Using Deep Convolutional Neural Network. IEEE Access, 9, 87079–87093.
KUMAR, C., UR REHMAN, F., KUMAR, S., MEHMOOD, A., & SHABIR, G., 2018. Analysis of MFCC and BFCC in a speaker identification system. 2018 International Conference on Computing, Mathematics and Engineering Technologies (ICoMET), 1–5.
MENG, H., YAN, T., YUAN, F., & WEI, H., 2019. Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network. IEEE Access, 7, 125868–125881. https://doi.org/10.1109/ACCESS.2019.2938007
MTSHALI, P. AND KHUBISA, F., 2019. A smart home appliance control system for physically disabled people. 2019 Conference on Information Communications Technology and Society (ICTAS), pp.1–5.
MUZAMAL, J. H., ASGHAR, M., KWONG, A., & RAZA, U. A., 2021. Microcontroller Based Intelligent Chinese-Speech Keywords Detector by Transferring the Mid-level Features of Deep Speech. 2021 International Conference on Communication Technologies (ComTech), 33–38.
NASSIF, A. B., SHAHIN, I., ATTILI, I., AZZEH, M., & SHAALAN, K., 2019. Speech Recognition Using Deep Neural Networks: A Systematic Review. IEEE Access, 7, 19143–19165.
PALAZ, D., MAGIMAI.-DOSS, M. AND COLLOBERT, R., 2015. Convolutional Neural Networks-based continuous speech recognition using raw speech signal. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4295–4299.
PRASOMPHAN, S., 2015. Improvement of speech emotion recognition with neural network classifier by using speech spectrogram. 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), pp.73–76.
RAMLEE, R.A., OTHMAN, M.A., LEONG, M.H., ISMAIL, M.M. AND RANJIT, S.S.S., 2013. Smart Home System using Android application. 2013 International Conference of Information and Communication Technology (ICoICT), pp.277–280.
RAMLEE, R.A., TANG, D.H. AND ISMAIL, M.M., 2012. Smart Home system for disabled people via Wireless bluetooth. 2012 International Conference on System Engineering and Technology (ICSET), pp.1–4.
WARDEN, P., & SITUNAYAKE, D. (N.D.). TinyML Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers PREVIEW OF FIRST SIX CHAPTERS Buy the full book at tinymlbook.com.
ZAHID, L., MAQSOOD, M., DURRANI, M. Y., BAKHTYAR, M., BABER, J., JAMAL, H., MEHMOOD, I., & SONG, O.-Y., 2020. A Spectrogram-Based Deep Feature Assisted Computer-Aided Diagnostic System for Parkinson’s Disease. IEEE Access, 8, 35482–35495.
ZOU, Z., WANG, Q., QIN, T., WANG, Q., ZOU, B., ZHOU, M. Speed Recognition System of Smart Home. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu, China, 2019, pp. 2464-2468.
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