Klasifikasi Suara Sirene Kendaraan berbasis MFCC untuk Meningkatkan Efisiensi Sistem Keamanan Lalu Lintas
Kata Kunci:
Suara Sirene, Mel Frequency Cepstral Coefficient, Convolutional Neural Network, MikrokomputerAbstrak
Ketertiban berlalu lintas di jalan raya merupakan salah satu faktor krusial dalam membangun keamanan dan kenyamanan dalam berlalu lintas. Hal tersebut dapat dilihat pada situasi lalu lintas yang padat seperti di perkotaan besar. Terlebih lagi jika terdapat situasi darurat yang membutuhkan kendaraan darurat dalam menangani hal tersebut. Maka dari itu, dibutuhkan sebuah sistem untuk mengenali dan melakukan klasifikasi kendaraan darurat sesuai dengan prioritasnya berdasarkan suara sirenenya. Sistem pengenalan suara sirene merupakan salah satu teknologi dalam membantu mengatur dan mengelola keamanan berlalu lintas. Penelitian ini membahas mengenai klasifikasi suara sirene kendaraan darurat dan menyesuaikan prioritasnya menggunakan metode MFCC yang dibantu dengan pelatihan dan evaluasi suara menggunakan CNN. Hasil dari penelitian ini yaitu sistem dapat mengenali suara sirene dengan baik, yang memiliki akurasi sebesar 94% terhadap suara sirene yang dikenali. Hal ini menandakan bahwa sistem dapat membantu pengguna dalam mengatur dan mengola lalu lintas agar terjadinya kenyamanan dan keamanan berlalu lintas.
Referensi
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Asif, M., Usaid, M., Rashid, M., Rajab, T., Hussain, S. and Wasi, S., 2022. Large-scale audio dataset for emergency vehicle sirens and road noises. Scientific Data, 9(1), pp.1–9. https://doi.org/10.1038/s41597-022-01727-2.
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Choudhury, K. and Nandi, D., 2023. Review of Emergency Vehicle Detection Techniques by Acoustic Signals. Transactions of the Indian National Academy of Engineering, [online] 8(4), pp.535–550. https://doi.org/10.1007/s41403-023-00424-9.
Elektronik, J., Udayana, I.K., Kadek, N., Dewi, Y., Ketut, I. and Suhartana, G., 2023. Idenfikasi Nada Dasar Kendang Menggunakan MFCC dan KNN. Jurnal Elektronik Ilmu Komputer Udayana, 11(4), pp.797–802.
Faricha, A., Suwito, Rivai, M., Nanda, M.A., Purwanto, D. and Anhar, R.P.R., 2018. Design of electronic nose system using gas chromatography principle and Surface Acoustic Wave sensor. Telkomnika (Telecommunication Computing Electronics and Control), 16(4), pp.1458–1467. https://doi.org/10.12928/TELKOMNIKA.v16i4.7127.
Fatimah, B., Preethi, A., Hrushikesh, V., Akhilesh Singh, B. and Kotion, H.R., 2020. An automatic siren detection algorithm using Fourier Decomposition Method and MFCC. 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020. https://doi.org/10.1109/ICCCNT49239.2020.9225414.
Howard, C., Maddern, A.J. and Privopoulos, E.P., 2014. Acoustic Characteristics for Effective Ambulance Sirens. (August 2011).
Hua, F. and Li, L., 2023. Sound anomaly detection of industrial products based on MFCC fusion short-time energy feature extraction. (1), pp.861–864. https://doi.org/10.1109/tocs56154.2022.10016076.
Li, Q., Yang, Y., Lan, T., Zhu, H., Wei, Q., Qiao, F., Liu, X. and Yang, H., 2020. MSP-MFCC: Energy-Efficient MFCC Feature Extraction Method with Mixed-Signal Processing Architecture for Wearable Speech Recognition Applications. IEEE Access, 8, pp.48720–48730. https://doi.org/10.1109/ACCESS.2020.2979799.
Massoudi, M., Verma, S. and Jain, R., 2021. Urban Sound Classification using CNN. Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, pp.583–589. https://doi.org/10.1109/ICICT50816.2021.9358621.
Miyazaki, K., Toda, T., Hayashi, T. and Takeda, K., 2019. Environmental sound processing and its applications. IEEJ Transactions on Electrical and Electronic Engineering, 14(3), pp.340–351. https://doi.org/10.1002/tee.22868.
Nurrizqy, I.M., Prasetio, B.H. and Mardi Putri, R.R., 2023. Sistem Kontrol Perangkat Inframerah Menggunakan Speech Recognition dengan Spectrogram dan Convolutional Neural Network Berbasis Mikrokontroler. Jurnal Teknologi Informasi dan Ilmu Komputer, 10(5), pp.955–962. https://doi.org/10.25126/jtiik.20231056909.
Pang, C., Liu, H. and Li, X., 2019. Multitask learning of time-frequency CNN for sound source localization. IEEE Access, 7(Ild), pp.40725–40737. https://doi.org/10.1109/ACCESS.2019.2905617.
Permana, M. fajar, Fiolana, F. alif and W.K., D.A., 2022. Klasifikasi Suara Sirene Menggunakan Stft (Short-Term Fourier Transform). Jurnal Ilmiah Sistem Informasi, 1(3), pp.44–58. https://doi.org/10.51903/juisi.v1i3.414.
Pramanick, D., Ansar, H., Kumar, H., Pranav, S., Tengshe, R. and Fatimah, B., 2021. Deep learning based urban sound classification and ambulance siren detector using spectrogram. 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, pp.1–6. https://doi.org/10.1109/ICCCNT51525.2021.9579778.
Pratiwi, H.I., Budiharto, W., Kartowisastro, I.H. and Soewito, B., 2024. Short Time Fourier Transform in Reinvigorating Distinctive Facts of Individual Spectral Centroid of Mel Frequency Numeric for Security Authentication. International Journal of Innovative Computing, Information and Control, 20(1), pp.213–229. https://doi.org/10.24507/ijicic.20.01.213.
Ramirez, A.E., Donati, E. and Chousidis, C., 2022. A siren identification system using deep learning to aid hearing-impaired people. Engineering Applications of Artificial Intelligence, [online] 114(March), p.105000. https://doi.org/10.1016/j.engappai.2022.105000.
Ranny, R., Suwardi, I.S., Rajab, T.L.E. and Lestari, D.P., 2019. Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi. JUITA : Jurnal Informatika, 7(1), p.1. https://doi.org/10.30595/juita.v7i1.3491.
Sathruhan, S., Herath, O.K., Sivakumar, T. and Thibbotuwawa, A., 2022. Emergency Vehicle Detection using Vehicle Sound Classification: A Deep Learning Approach. 6th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI-2022, pp.1–6. https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002605.
Shabiyya, S.H., Prasetio, B.H. and Widasari, E.R., 2023. Harnessing the Power of CNN-Transformer Encoders in Stress Speech Analysis. Proceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023, pp.147–151. https://doi.org/10.1109/ICITCOM60176.2023.10442454.
Shah, A. and Singh, A., 2023. sireNNet-Emergency Vehicle Siren Classification Dataset For Urban Applications. 1. https://doi.org/10.17632/J4YDZZV4KB.1.
Supreeth, H. V., Rao, S., Chethan, K.S. and Purushotham, U., 2020. Identification of Ambulance Siren sound and Analysis of the signal using statistical method. Proceedings of International Conference on Intelligent Engineering and Management, ICIEM 2020, pp.198–202. https://doi.org/10.1109/ICIEM48762.2020.9160070.
Tran, V.T. and Tsai, W.H., 2020. Acoustic-Based Emergency Vehicle Detection Using Convolutional Neural Networks. IEEE Access, 8, pp.75702–75713. https://doi.org/10.1109/ACCESS.2020.2988986.
Anggoro, W., Sukmawati, N., Satriyo, A. and Sutikno, 2014. Aplikasi Speech Recognition Bahasa Indonesia Dengan Metode Mel-Frequency Cepstral Coefficient Dan Linear Vector. Seminar Nasional Ilmu Komputer Undip, pp.61–66.
Asif, M., Usaid, M., Rashid, M., Rajab, T., Hussain, S. and Wasi, S., 2022. Large-scale audio dataset for emergency vehicle sirens and road noises. Scientific Data, 9(1), pp.1–9. https://doi.org/10.1038/s41597-022-01727-2.
Astawa, I.N.G.A., Radhitya, M.L., Ardana, I.W.R. and Dwiyanto, F.A., 2021. Face Images Classification using VGG-CNN. Knowledge Engineering and Data Science, 4(1), p.49. https://doi.org/10.17977/um018v4i12021p49-54.
Bhavya, M. and Anala, M.R., 2022. Deep Learning Approach for Sound Signal Processing. 2022 International Conference on Futuristic Technologies, INCOFT 2022, pp.1–4. https://doi.org/10.1109/INCOFT55651.2022.10094337.
Choudhury, K. and Nandi, D., 2023. Review of Emergency Vehicle Detection Techniques by Acoustic Signals. Transactions of the Indian National Academy of Engineering, [online] 8(4), pp.535–550. https://doi.org/10.1007/s41403-023-00424-9.
Elektronik, J., Udayana, I.K., Kadek, N., Dewi, Y., Ketut, I. and Suhartana, G., 2023. Idenfikasi Nada Dasar Kendang Menggunakan MFCC dan KNN. Jurnal Elektronik Ilmu Komputer Udayana, 11(4), pp.797–802.
Faricha, A., Suwito, Rivai, M., Nanda, M.A., Purwanto, D. and Anhar, R.P.R., 2018. Design of electronic nose system using gas chromatography principle and Surface Acoustic Wave sensor. Telkomnika (Telecommunication Computing Electronics and Control), 16(4), pp.1458–1467. https://doi.org/10.12928/TELKOMNIKA.v16i4.7127.
Fatimah, B., Preethi, A., Hrushikesh, V., Akhilesh Singh, B. and Kotion, H.R., 2020. An automatic siren detection algorithm using Fourier Decomposition Method and MFCC. 2020 11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020. https://doi.org/10.1109/ICCCNT49239.2020.9225414.
Howard, C., Maddern, A.J. and Privopoulos, E.P., 2014. Acoustic Characteristics for Effective Ambulance Sirens. (August 2011).
Hua, F. and Li, L., 2023. Sound anomaly detection of industrial products based on MFCC fusion short-time energy feature extraction. (1), pp.861–864. https://doi.org/10.1109/tocs56154.2022.10016076.
Li, Q., Yang, Y., Lan, T., Zhu, H., Wei, Q., Qiao, F., Liu, X. and Yang, H., 2020. MSP-MFCC: Energy-Efficient MFCC Feature Extraction Method with Mixed-Signal Processing Architecture for Wearable Speech Recognition Applications. IEEE Access, 8, pp.48720–48730. https://doi.org/10.1109/ACCESS.2020.2979799.
Massoudi, M., Verma, S. and Jain, R., 2021. Urban Sound Classification using CNN. Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021, pp.583–589. https://doi.org/10.1109/ICICT50816.2021.9358621.
Miyazaki, K., Toda, T., Hayashi, T. and Takeda, K., 2019. Environmental sound processing and its applications. IEEJ Transactions on Electrical and Electronic Engineering, 14(3), pp.340–351. https://doi.org/10.1002/tee.22868.
Nurrizqy, I.M., Prasetio, B.H. and Mardi Putri, R.R., 2023. Sistem Kontrol Perangkat Inframerah Menggunakan Speech Recognition dengan Spectrogram dan Convolutional Neural Network Berbasis Mikrokontroler. Jurnal Teknologi Informasi dan Ilmu Komputer, 10(5), pp.955–962. https://doi.org/10.25126/jtiik.20231056909.
Pang, C., Liu, H. and Li, X., 2019. Multitask learning of time-frequency CNN for sound source localization. IEEE Access, 7(Ild), pp.40725–40737. https://doi.org/10.1109/ACCESS.2019.2905617.
Permana, M. fajar, Fiolana, F. alif and W.K., D.A., 2022. Klasifikasi Suara Sirene Menggunakan Stft (Short-Term Fourier Transform). Jurnal Ilmiah Sistem Informasi, 1(3), pp.44–58. https://doi.org/10.51903/juisi.v1i3.414.
Pramanick, D., Ansar, H., Kumar, H., Pranav, S., Tengshe, R. and Fatimah, B., 2021. Deep learning based urban sound classification and ambulance siren detector using spectrogram. 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, pp.1–6. https://doi.org/10.1109/ICCCNT51525.2021.9579778.
Pratiwi, H.I., Budiharto, W., Kartowisastro, I.H. and Soewito, B., 2024. Short Time Fourier Transform in Reinvigorating Distinctive Facts of Individual Spectral Centroid of Mel Frequency Numeric for Security Authentication. International Journal of Innovative Computing, Information and Control, 20(1), pp.213–229. https://doi.org/10.24507/ijicic.20.01.213.
Ramirez, A.E., Donati, E. and Chousidis, C., 2022. A siren identification system using deep learning to aid hearing-impaired people. Engineering Applications of Artificial Intelligence, [online] 114(March), p.105000. https://doi.org/10.1016/j.engappai.2022.105000.
Ranny, R., Suwardi, I.S., Rajab, T.L.E. and Lestari, D.P., 2019. Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi. JUITA : Jurnal Informatika, 7(1), p.1. https://doi.org/10.30595/juita.v7i1.3491.
Sathruhan, S., Herath, O.K., Sivakumar, T. and Thibbotuwawa, A., 2022. Emergency Vehicle Detection using Vehicle Sound Classification: A Deep Learning Approach. 6th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI-2022, pp.1–6. https://doi.org/10.1109/SLAAI-ICAI56923.2022.10002605.
Shabiyya, S.H., Prasetio, B.H. and Widasari, E.R., 2023. Harnessing the Power of CNN-Transformer Encoders in Stress Speech Analysis. Proceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023, pp.147–151. https://doi.org/10.1109/ICITCOM60176.2023.10442454.
Shah, A. and Singh, A., 2023. sireNNet-Emergency Vehicle Siren Classification Dataset For Urban Applications. 1. https://doi.org/10.17632/J4YDZZV4KB.1.
Supreeth, H. V., Rao, S., Chethan, K.S. and Purushotham, U., 2020. Identification of Ambulance Siren sound and Analysis of the signal using statistical method. Proceedings of International Conference on Intelligent Engineering and Management, ICIEM 2020, pp.198–202. https://doi.org/10.1109/ICIEM48762.2020.9160070.
Tran, V.T. and Tsai, W.H., 2020. Acoustic-Based Emergency Vehicle Detection Using Convolutional Neural Networks. IEEE Access, 8, pp.75702–75713. https://doi.org/10.1109/ACCESS.2020.2988986.
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