Citation: | XING Yulong, WANG Jian, ZHAO Huibing, ZHU Linfu. Cab Signal Denoising Process Based on Fully Convolutional Networks[J]. Journal of Southwest Jiaotong University, 2021, 56(2): 444-450. doi: 10.3969/j.issn.0258-2724.20191111 |
邱宽民. JT1-CZ2000型机车信号车载系统[M]. 北京: 中国铁道出版社, 2010: 1-12.
|
ZHAO Linhai, LI Zhankui, LIU Weining. The compensation capacitors fault detection method of jointless track circuit based on DBWT and WR[C]//IEEE International Conference on Intelligent Computing & Intelligent Systems. Shanghai: IEEE, 2009: 875-879.
|
剌博. 基于EMD降噪的轨道移频信号检测算法研究[D]. 西安: 西安理工大学, 2014.
|
轩春霞,王小敏,杨扬,等. 基于稀疏分解的轨道移频信号降噪算法研究[J]. 计算机测量与控制,2014,22(9): 2870-2874. doi: 10.3969/j.issn.1671-4598.2014.09.048
XUAN Chunxia, WANG Xiaomin, YANG Yang, et al. Denoising algorithm for track circuit frequency:shift signal based on sparse decomposition[J]. Computer Measurement & Control, 2014, 22(9): 2870-2874. doi: 10.3969/j.issn.1671-4598.2014.09.048
|
NAIK D C, MURTHY A S, NUTHAKKI R. Modified magnitude spectral subtraction methods for speech enhancement[C]//2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques(ICEECCOT). Mysuru: [s.n.], 2017: 274-279.
|
EPHRAIM Y, MALAH D. Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1984, 32(6): 1109-1121.
|
SCALART P, FILHO J V. Speech enhancement based on a priori signal to noise estimation[C]//IEEE International Conference on Acoustics. Atlanta: IEEE, 1996: 629-632.
|
IBARROLA F J, DI PERSIA L, SPIES R D. A Bayesian approach to convolutive nonnegative matrix factorization for blind speech dereverberation[J]. Signal Processing, 2018, 151(4): 89-98.
|
HOU J C, WANG S S, LAI Y H, et al. Audio-visual speech enhancement using multimodal deep convolutional neural networks[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(2): 117-128.
|
PALAZ D, COLLOBERT R, DOSS M M. Estimating phoneme class conditional probabilities from raw speech signal using convolutional neural networks[C]//14th Annual Conference of the International Speech Communication Association. Lyon: Interspeech, 2013: 1765-1769.
|
OORD A V D, DIELEMAN S, ZEN H, et al. WaveNet: a generative model for raw audio[J]. Computer Science, 2016, 1: 1-15.
|
FU S W, WANG T W, TSAO Y, et al. End-to-end waveform utterance enhancement for direct evaluation metrics optimization by fully convolutional neural networks[J]. IEEE/ACM Transactions on Audio Speech & Language Processing, 2018, 26(9): 1570-1584.
|
赵自信. ZPW—2000A无绝缘移频自动闭塞系统的技术综述[J]. 铁路通信信号工程技术, 2003, 2003(增刊1): 12-19.
ZHAO Zixin. A review of ZPW-2000 automatic block with jointless frequency-shift system[J]. Railway Signalling & Communication Engineering, 2003, 2003(S1): 12-19.
|
中华人民共和国铁道部. ZPW-2000轨道电路技术条件: TB/T 3206—2008[S]. 北京: 中国铁道出版社, 2008
|
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine, 2015, 39(4): 640-651.
|
THAKKAR V, TEWARY S, CHAKRABORTY C.Batch normalization in convolutional neural networks: a comparative study with CIFAR-10 data[C]//2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT). Kolkata: [s.n.], 2018: 1-5.
|
ZHANG Yudong, PAN Chichun, SUN Junding, et al. Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU[J]. Journal of Computational Science, 2018, 28(9): 1-10.
|