Penerapan Metode Hidden Markov Model Pada Sistem Pengenalan Suara Sirene Kendaraan Darurat
Kata Kunci:
Sirene, MFCC, HMM, Raspberry Pi 4 Model BAbstrak
Sirene merupakan sebuah alat yang dapat menghasilkan suara yang keras dengan tujuan untuk menunjukan tanda bahaya. Sirene digunakan untuk kendaraan darurat seperti ambulans, pemadam kebakaran, dan polisi. Sirene kendaraan ini memiliki bunyi yang berbeda dan memiliki penerapan yang berbeda. Tujuan dari penelitian ini adalah untuk merancang sebuah alat yang dapat mengenali jenis suara sirene kendaraan darurat yang dapat digunakan dalam setiap situasi. Dataset yang digunakan adalah SirenNet yang merupakan audio data suara sirene kendaraan berdurasi 3 detik. Alat ini akan menerapkan metode Mel-Frequency Ceptral Coefficient (MFCC) untuk mengekstraksikan fitur dari data suara, dan melakukan proses pengenalan terhadap data suara dengan algoritma Hidden Markov Model (HMM) untuk diimplementasikan ke dalam Raspberry Pi 4 dan dapat dioperasikan melalui layar LCD Display yang terhubung dengan sistem. Penelitian ini menunjukan model HMM yang telah dibuat mendapatkan nilai akurasi sebesar 86%, dan alat dapat memprediksi sebesar 73,3% dari 30 jenis data suara. Sistem memiliki akurasi dalam mendeteksi sirene ambulans sebesar 80%, pemadam kebakaran sebesar 90%, dan polisi sebesar 50%.
Referensi
Barai, B., Das, D., Das, N., Basu, S., Nasipuri, M. (2019). VQ/GMM-Based Speaker Identification with Emphasis on Language Dependency. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 883. Springer, Singapore. doi.org/10.1007/978-981-13-3702-4_8
Chamidy, T. (2016). “Metode Mel Frequency Cepstral Coeffisients (MFCC) Pada klasifikasi Hidden Markov Model (HMM) Untuk Kata Arabic pada Penutur Indonesia”. MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), 8(1), 36-39.
Davis, S. and Mermelstein, P. (1980) “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences”. IEEE Transactions on Acoustics, Speech and Signal Processing, 28, 357-366. dx.doi.org/10.1109/TASSP.1980.1163420
Howard, C. Q., Maddern, A. J., & Privopoulos, E. P. (2011). “Acoustic Characteristics For Effective Ambulance Sirens”. Acoustics Australia, 39(2).
Islam, Z., & Abdel-Aty, M. (2022). “Real-time emergency vehicle event detection using audio data”. arXiv preprint arXiv:2202.01367. doi: doi.org/10.48550/arXiv.2202.01367
Jollyta, D., Oktarina, D., & Johan, J. (2020). Tinjauan Kasus Model Speech Recognition: Hidden Markov Model. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 6(2), 202-209. doi: dx.doi.org/10.26418/jp.v6i2.39231
Kusuma, Dine T. (2021). "Fast Fourier Transform (FFT) dalam Transformasi Sinyal Frekuensi Suara sebagai Upaya Perolehan Average Energy (AE) Musik." Petir, vol. 14, no. 1, doi:10.33322/petir.v14i1.1022.
L. Shi, I. Ahmad, Y. He and K. Chang, (2018). "Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments," in Journal of Communications and Networks, vol. 20, no. 5, pp. 509-518, doi: 10.1109/JCN.2018.000075
L. Rabiner and B. Juang, (1986). "An introduction to hidden Markov models" in IEEE ASSP Magazine, vol. 3, no. 1, pp. 4-16, , doi: 10.1109/MASSP.1986.1165342
L. R. Rabiner, (1989). "A tutorial on hidden Markov models and selected applications in speech recognition," in Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, Feb., doi: 10.1109/5.18626
Manunggal, H. S. (2005). Perancangan dan pembuatan perangkat lunak pengenalan suara pembicara dengan menggunakan analisa MFCC feature extraction (Doctoral dissertation, Petra Christian University).
Mittal, U., and Chawla, P. (2023). “Acoustic based emergency vehicle detection using ensemble of deep learning models”. Procedia Computer Science, 218, 227-234. Available at: doi.org/10.1016/j.procs.2023.01.005
Permana, M., Fiolana, F., & W.K., Diah. (2022). “Klasifikasi Suara Sirene Menggunakan STFT (Short-Term Fourier Transform)”. Jurnal Ilmiah Sistem Informasi. 1. 44-58. Available at: doi.org/10.51903/juisi.v1i3.414
Prakasa, J. E. W. (2016). “ANTISIPASI KEDATANGAN KENDARAAN DARURAT MELALUI EMERGENCY MESSAGE PADA LINGKUNGAN VEHICULAR ADHOC NETWORK”. Jurnal SPIRIT, 8, 12-16.
Prasetyo, M. E. B. (2010). “Teori Dasar Hidden Markov Model”. Makalah II2092 Probabilitas dan Statistik.
Putra, D., & Resmawan, A. (2011). “Verifikasi biometrika suara menggunakan metode MFCC dan DTW”. Lontar Komputer, 2(1), 8-21.
Raspberry Pi Foundation. (2024). “Raspberry Pi 4 Model B Datasheet”. Diperoleh dari https://datasheets.raspberrypi.com/rpi4/raspberry-pi-4-datasheet.pdf
Republik Indonesia, (2009). “Undang-undang No.22 Tahun 2009 tentang Lalu Lintas dan Angkutan Jalan”. Jakarta.
SANJAYA, W.S.M., SALLEH, Z., (2014). “Implementasi Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (MFCC) dan Adaptive Neuro-Fuzzy Inferense System (ANFIS) sebagai Kontrol Lampu Otomatis”, Al-Hazen Journal of Physics, 1(1), pp.44-54.
Shah, Arya; Singh, Amanpreet (2023), “sireNNet-Emergency Vehicle Siren Classification Dataset For Urban Applications”, Mendeley Data, V1, doi: 10.17632/j4ydzzv4kb.1
S. Sathruhan, O. K. Herath, T. Sivakumar and A. Thibbotuwawa. (2022). "Emergency Vehicle Detection using Vehicle Sound Classification: A Deep Learning Approach," 2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI), Colombo, Sri Lanka, pp. 1-6, doi: 10.1109/SLAAI-ICAI56923.2022.10002605.
Tran, V.T. and Tsai, W.H., (2020). “Acoustic-based emergency vehicle detection using convolutional neural networks”. IEEE Access, 8, pp.75702-75713. doi: doi.org/10.1109/ACCESS.2020.2988986
Tran, V. T., Yan, Y. C., & Tsai, W. H. (2017). “Detection of ambulance and fire truck siren sounds using neural networks”. ARPN Journal of Engineering and Applied Sciences, 12(5).
Xia L, Chen G, Xu X, Cui J, Gao Y. (2020). “Audiovisual speech recognition: A review and forecast”. International Journal of Advanced Robotic Systems. 17(6). doi:10.1177/1729881420976082
Y. Atahan, A. Elbir, A. Enes Keskin, O. Kiraz, B. Kirval and N. Aydin. (2021). "Music Genre Classification Using Acoustic Features and Autoencoders," Innovations in Intelligent Systems and Applications Conference (ASYU), Elazig, Turkey, 2021, pp. 1-5, doi: 10.1109/ASYU52992.2021.9598979.
Z. K. Abdul and A. K. Al-Talabani, (2022). "Mel Frequency Cepstral Coefficient and its Applications: A Review," in IEEE Access, vol. 10, pp. 122136-122158, 2022, doi: 10.1109/ACCESS.2022.3223444.
Barai, B., Das, D., Das, N., Basu, S., Nasipuri, M. (2019). VQ/GMM-Based Speaker Identification with Emphasis on Language Dependency. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 883. Springer, Singapore. doi.org/10.1007/978-981-13-3702-4_8
Chamidy, T. (2016). “Metode Mel Frequency Cepstral Coeffisients (MFCC) Pada klasifikasi Hidden Markov Model (HMM) Untuk Kata Arabic pada Penutur Indonesia”. MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), 8(1), 36-39.
Davis, S. and Mermelstein, P. (1980) “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences”. IEEE Transactions on Acoustics, Speech and Signal Processing, 28, 357-366. dx.doi.org/10.1109/TASSP.1980.1163420
Howard, C. Q., Maddern, A. J., & Privopoulos, E. P. (2011). “Acoustic Characteristics For Effective Ambulance Sirens”. Acoustics Australia, 39(2).
Islam, Z., & Abdel-Aty, M. (2022). “Real-time emergency vehicle event detection using audio data”. arXiv preprint arXiv:2202.01367. doi: doi.org/10.48550/arXiv.2202.01367
Jollyta, D., Oktarina, D., & Johan, J. (2020). Tinjauan Kasus Model Speech Recognition: Hidden Markov Model. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 6(2), 202-209. doi: dx.doi.org/10.26418/jp.v6i2.39231
Kusuma, Dine T. (2021). "Fast Fourier Transform (FFT) dalam Transformasi Sinyal Frekuensi Suara sebagai Upaya Perolehan Average Energy (AE) Musik." Petir, vol. 14, no. 1, doi:10.33322/petir.v14i1.1022.
L. Shi, I. Ahmad, Y. He and K. Chang, (2018). "Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments," in Journal of Communications and Networks, vol. 20, no. 5, pp. 509-518, doi: 10.1109/JCN.2018.000075
L. Rabiner and B. Juang, (1986). "An introduction to hidden Markov models" in IEEE ASSP Magazine, vol. 3, no. 1, pp. 4-16, , doi: 10.1109/MASSP.1986.1165342
L. R. Rabiner, (1989). "A tutorial on hidden Markov models and selected applications in speech recognition," in Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, Feb., doi: 10.1109/5.18626
Manunggal, H. S. (2005). Perancangan dan pembuatan perangkat lunak pengenalan suara pembicara dengan menggunakan analisa MFCC feature extraction (Doctoral dissertation, Petra Christian University).
Mittal, U., and Chawla, P. (2023). “Acoustic based emergency vehicle detection using ensemble of deep learning models”. Procedia Computer Science, 218, 227-234. Available at: doi.org/10.1016/j.procs.2023.01.005
Permana, M., Fiolana, F., & W.K., Diah. (2022). “Klasifikasi Suara Sirene Menggunakan STFT (Short-Term Fourier Transform)”. Jurnal Ilmiah Sistem Informasi. 1. 44-58. Available at: doi.org/10.51903/juisi.v1i3.414
Prakasa, J. E. W. (2016). “ANTISIPASI KEDATANGAN KENDARAAN DARURAT MELALUI EMERGENCY MESSAGE PADA LINGKUNGAN VEHICULAR ADHOC NETWORK”. Jurnal SPIRIT, 8, 12-16.
Prasetyo, M. E. B. (2010). “Teori Dasar Hidden Markov Model”. Makalah II2092 Probabilitas dan Statistik.
Putra, D., & Resmawan, A. (2011). “Verifikasi biometrika suara menggunakan metode MFCC dan DTW”. Lontar Komputer, 2(1), 8-21.
Raspberry Pi Foundation. (2024). “Raspberry Pi 4 Model B Datasheet”. Diperoleh dari https://datasheets.raspberrypi.com/rpi4/raspberry-pi-4-datasheet.pdf
Republik Indonesia, (2009). “Undang-undang No.22 Tahun 2009 tentang Lalu Lintas dan Angkutan Jalan”. Jakarta.
SANJAYA, W.S.M., SALLEH, Z., (2014). “Implementasi Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (MFCC) dan Adaptive Neuro-Fuzzy Inferense System (ANFIS) sebagai Kontrol Lampu Otomatis”, Al-Hazen Journal of Physics, 1(1), pp.44-54.
Shah, Arya; Singh, Amanpreet (2023), “sireNNet-Emergency Vehicle Siren Classification Dataset For Urban Applications”, Mendeley Data, V1, doi: 10.17632/j4ydzzv4kb.1
S. Sathruhan, O. K. Herath, T. Sivakumar and A. Thibbotuwawa. (2022). "Emergency Vehicle Detection using Vehicle Sound Classification: A Deep Learning Approach," 2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI), Colombo, Sri Lanka, pp. 1-6, doi: 10.1109/SLAAI-ICAI56923.2022.10002605.
Tran, V.T. and Tsai, W.H., (2020). “Acoustic-based emergency vehicle detection using convolutional neural networks”. IEEE Access, 8, pp.75702-75713. doi: doi.org/10.1109/ACCESS.2020.2988986
Tran, V. T., Yan, Y. C., & Tsai, W. H. (2017). “Detection of ambulance and fire truck siren sounds using neural networks”. ARPN Journal of Engineering and Applied Sciences, 12(5).
Xia L, Chen G, Xu X, Cui J, Gao Y. (2020). “Audiovisual speech recognition: A review and forecast”. International Journal of Advanced Robotic Systems. 17(6). doi:10.1177/1729881420976082
Y. Atahan, A. Elbir, A. Enes Keskin, O. Kiraz, B. Kirval and N. Aydin. (2021). "Music Genre Classification Using Acoustic Features and Autoencoders," Innovations in Intelligent Systems and Applications Conference (ASYU), Elazig, Turkey, 2021, pp. 1-5, doi: 10.1109/ASYU52992.2021.9598979.
Z. K. Abdul and A. K. Al-Talabani, (2022). "Mel Frequency Cepstral Coefficient and its Applications: A Review," in IEEE Access, vol. 10, pp. 122136-122158, 2022, doi: 10.1109/ACCESS.2022.3223444.
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