|
[1] L. Rabiner and B.-H. Juang, Fundamentals of speech recognition. 1993.
[2] M. H. Savoji, "A robust algorithm for accurate endpointing of speech signals," Speech communication, vol. 8, no. 1, pp. 45-60, 1989.
[3] R. E. Ziemer and W. H. Tranter, Principles of Communications: Systems, Modulation and Noise, 7th ed. 2014.
[4] S. Haykin and M. Moher, Communication Systems, 5th ed. 2017.
[5] C. H. Kao and N. P. School, Performance Analysis of a JTIDS/Link-16-type Waveform Transmitted Over Slow, Flat Nakagami Fading Channels in the Presence of Narrowband Interference. Naval postgraduate school monterey ca, 2008.
[6] N. Grumman, "Understanding voice and data link networking," Accessed on May, vol. 9, p. 2014, 2013.
[7] 梅文华 and 蔡善法, JTIDS/Link16 数据链. 北京: 国防工业出版社, 2007.
[8] L. R. Rabiner and M. R. Sambur, "An algorithm for determining the endpoints of isolated utterances," Bell System Technical Journal, vol. 54, no. 2, pp. 297-315, 1975.
[9] J. Haigh and J. Mason, "Robust voice activity detection using cepstral features," in Proceedings of TENCon'93. IEEE Region 10 International Conference on Computers, Communications and Automation, 1993, vol. 3, pp. 321-324.
[10] J.-l. Shen, J.-w. Hung, and L.-s. Lee, "Robust entropy-based endpoint detection for speech recognition in noisy environments," in Fifth international conference on spoken language processing, 1998.
[11] C. Jia and B. Xu, "An improved entropy-based endpoint detection algorithm," in International Symposium on Chinese Spoken Language Processing, 2002.
[12] H. Liang-Sheng and Y. Chung-Ho, "A novel approach to robust speech endpoint detection in car environments," in 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2000, vol. 3, pp. 1751-1754
[13] B. P. Bogert, "The quefrency alanysis of time series for echoes; Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking," Time series analysis, pp. 209-243, 1963.
[14] C. E. Shannon, "A mathematical theory of communication," Bell system technical journal, vol. 27, no. 3, pp. 379-423, 1948.
[15] L. Rabiner and M. Sambur, "Voiced-unvoiced-silence detection using the Itakura LPC distance measure," in ICASSP'77. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1977, vol. 2, pp. 323-326.
[16] J.-C. Junqua, B. Mak, and B. Reaves, "A robust algorithm for word boundary detection in the presence of noise," IEEE Transactions on speech and audio processing, vol. 2, no. 3, pp. 406-412, 1994.
[17] S. Raschka and V. Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd, 2019.
[18] S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," 2015.
[19] R. Bellman, "Dynamic programming," Science, vol. 153, no. 3731, pp. 34-37, 1966.
[20] R. E. Bellman, Adaptive control processes: a guided tour. Princeton university press, 2015.
[21] G. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE transactions on information theory, vol. 14, no. 1, pp. 55-63, 1968.
[22] I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," Journal of machine learning research, vol. 3, pp. 1157-1182, 2003.
[23] A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, and M. Lang, "Benchmark for filter methods for feature selection in high-dimensional classification data," Computational Statistics & Data Analysis, vol. 143, 2020.
[24] V. Spruyt, "The Curse of Dimensionality in classification," Computer vision for dummies, vol. 21, no. 3, pp. 35-40, 2014.
[25] C. Dai, L. Luo, H. Peng, and Q. Sun, "A Method Based on Support Vector Machine for Voice Activity Detection on Isolated Words," in 2018 13th International Conference on Computer Science & Education (ICCSE), 2018, pp. 1-4.
[26] J. Dey, M. S. B. Hossain, and M. A. Haque, "An Ensemble SVM-based Approach for Voice Activity Detection," in 2018 10th International Conference on Electrical and Computer Engineering (ICECE), 2018, pp. 297-300.
[27] A. Géron, Hands-On Machine Learning with Scikit-Learn & TensorFlow. 2017.
[28] C. J. Burges, "A tutorial on support vector machines for pattern recognition," Data mining and knowledge discovery, vol. 2, no. 2, pp. 121-167, 1998.
[29] V. Vapnik, The nature of statistical learning theory. Springer science & business media, 2013.
[30] C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, no. 3, pp. 273-297, 1995.
[31] B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the fifth annual workshop on Computational learning theory, 1992, pp. 144-152.
[32] L. Rokach and O. Maimon, "Top-down induction of decision trees classifiers-a survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 35, no. 4, pp. 476-487, 2005.
[33] J. Bergstra and Y. Bengio, "Random search for hyper-parameter optimization," Journal of machine learning research, vol. 13, no. Feb, pp. 281-305, 2012.
[34] E. Brochu, V. M. Cora, and N. De Freitas, "A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning," 2010.
[35] J. Snoek, H. Larochelle, and R. P. Adams, "Practical bayesian optimization of machine learning algorithms," in Advances in neural information processing systems, 2012, pp. 2951-2959.
[36] C. J. Van Rijsbergen, Information retrieval. London; Boston: Butterworths, 1979.
[37] D. E. Knuth, "Seminumerical algorithm," The art of computer programming, 1981.
[38] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," 1995, vol. 14, no. 2, pp. 1137-1145: Montreal, Canada.
|