Acknowledgement
이 논문은 창원대학교 2021-2022년도 창원대학교 자율연구과제 연구비 지원으로 수행된 연구 결과임.
References
- Aicha, A. B. (2018). Noninvasive detection of potentially precancerous lesions of vocal fold based on glottal wave signal and SVM approaches. Procedia Computer Science, 126, 586-595. https://doi.org/10.1016/j.procs.2018.07.293
- Al-Nasheri, A., Muhammad, G., Alsulaiman, M., Ali, Z., Mesallam, T. A., Farahat, M., Malki, K. H., ... Bencherif, M. A. (2017). An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification. Journal of Voice, 31(1), 113.e9-113.e18.
- Bezdek, J. C., Keller, J., Krisnapuram, R., Pal, N. R. (2005). Fuzzy models and algorithms for pattern recognition and image processing. (pp. 442-490). New York, NY: Springer.
- Fang, S. H., Tsao, Y., Hsiao, M. J., Chen, J. Y., Lai, Y. H., Lin, F. C., & Wang, C. T. (2019). Detection of pathological voice using cepstrum vectors: A deep learning approach. Journal of Voice, 33(5), 634-641. https://doi.org/10.1016/j.jvoice.2018.02.003
- Hegde, S., Shetty, S., Rai, S., & Dodderi, T. (2019). A survey on machine learning approaches for automatic detection of voice disorders. Journal of Voice, 33(6), 947.e11-947.e33.
- Jeon, B. U., Kang, J. S., & Chung, K. (2021). AutoLM and CNN-based soft-voting ensemble classification model for road traffic emerging risk detection. Journal of Convergence for Information Technology, 11(7), 14-20.
- Jo, C., Kim, K., Kim, D., & Wang, S. (2001, September). Screening of pathological voice from ARS using neural networks. Proceedings of the Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) 2nd International Workshop (pp. 241-245).
- Florence, Italy. Jung, H., Choi, M. K., Kim, J., Kwon, S., & Jung, W. (2020). CNN-based weighted ensemble technique for ImageNet classification. IEMEK Journal of Embedded Systems and Applications, 15(4), 197-204. https://doi.org/10.14372/IEMEK.2020.15.4.197
- Kim, H. B., Jeon, J., Han, Y. J., Joo, Y. H., Lee, J., Lee, S., & Im, S. (2020). Convolutional neural network classifies pathological voice change in laryngeal cancer with high accuracy. Journal of Clinical Medicine, 9(11), 3415.
- Ko, H., Ha, H., Cho, H., Seo, K., & Lee, J. (2019, May). Pneumonia detection with weighted voting ensemble of CNN models. Proceedings of the 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 306-310). Chengdu, China.
- Lee, J. Y. (2021). Experimental evaluation of deep learning methods for an intelligent pathological voice detection system using the Saarbruecken voice database. Applied Sciences, 11(15), 7149. https://doi.org/10.3390/app11157149
- Librosa. (2021). Librosa: Audio and music processing in Python. Retrieved from http://librosa.org/
- Liu, F., Liu, Y., & Sang, H. (2020). Multi-classifier decision-level fusion classification of workpiece surface defects based on a convolutional neural network. Symmetry, 12(5), 867. https://doi.org/10.3390/sym12050867
- Lv, X., Ming, D., Lu, T., Zhou, K., Wang, M., & Bao, H. (2018). A new method for region-based majority voting CNNs for very high resolution image classification. Remote Sensing, 10(12), 1946. https://doi.org/10.3390/rs10121946
- Massachusetts Eye and Ear Infirmary. (1994). Voice disorders database, version.1.03 (CD-ROM). Lincoln Park, NJ: Kay Elemetrics.
- Morvant, E., Habrard, A., & Ayache, S. (2014, August). Majority vote of diverse classifiers for late fusion. Proceedings of the Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) (p. 20). Joensuu, Finland.
- Roy, S., Sayim, M. I., & Akhand, M. A. H. (2019, May). Pathological voice classification using deep learning. Proceedings of the 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). Dhaka, Bangladesh.
- Ruta, D., & Gabrys, B. (2000). An overview of classifier fusion methods. Computing and Information Systems, 7(1), 1-10.
- Saarbruecken Voice Database. (2020). Saarbruecken Voice Database. Retrieved from http://www.stimmdatenbank.coli.uni-saarland.de/
- Saldanha, J. C., Ananthakrishna, T., & Pinto, R. (2014). Vocal fold pathology assessment using mel-frequency cepstral coefficients and linear predictive cepstral coefficients features. Journal of Medical Imaging and Health Informatics, 4(2), 168-173. https://doi.org/10.1166/jmihi.2014.1253
- Scikit learn. (2022). Ensemble methods. Retrieved from https://scikit-learn.org/stable/modules/ensemble.html
- Su, Y., Zhang, K., Wang, J., & Madani, K. (2019). Environment sound classification using a two-stream CNN based on decision-level fusion. Sensors, 19(7), 1733. https://doi.org/10.3390/s19071733
- Szmurlo, R., & Osowski, S. (2021, September). Deep CNN ensemble for recognition of face images. Proceedings of the 2021 22nd International Conference on Computational Problems of Electrical Engineering (CPEE) (pp. 1-4). Hradek u Susice, Czech Republic.
- Tensorflow. (2021). Retrieved from http://www.tensorflow.org/
- Wu, H., Soraghan, J., Lowit, A., & Di Caterina, G. (2018, July). Convolutional neural networks for pathological voice detection. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1-4). Honolulu, HI.