[1] 舒新玲,周岱.风速时程AR模型及其快速实现[J].空间结构,2003,(04):27-32+46. SHU X-L,ZHOU D.AR model of wind speed time series and its rapid implementation[J].Spatial Structures,2003,(04):27-32+46.(in Chinese)
[2] 马秉伟,刘会金,周莉,崔福鑫.一种基于自回归模型的间谐波谱估计的改进算法[J].中国电机工程学报,2005,(15):79-83. MA B-W,LIU H-J,ZHOU L,CUI F-X.An improved algorithm of interharmonic spectral estimation based on AR model[J].Proceedings of the CSEE,2005,(15):79-83.(in Chinese)
[3] 李星秀,韦志辉.基于局部自回归模型的压缩感知视频图像递归重建算法[J].电子学报,2012,40(9):1795-1800. LI Xing-xiu,WEI Zhi-hu.Compressed sensing video images recursive reconstruction algorithm based on local autoregressive model[J].Acta Electronica Sinica,2012,40(9):1795-1800.(in Chinese)
[4] 吴桐雨,王健.中国物流业、经济增长与技术创新——基于2002~2017年向量自回归模型的实证研究[J].工业技术经济,2019,38(03):116-122.
[5] 陈辉,张博霞.自回归预测多级矢量量化线谱频率编码技术[J].西安科技大学学报,2017,37(05):736-741. CHEN H,ZHANG B-X.Technology of multi-stage vector quantization with autoregressive prediction for linear spectrum frequency[J].Journal of Xi'an University of Science and Technology,2017,37(05):736-741.(in Chinese)
[6] SCHROEDER M,ATAL B S.Code-excited linear prediction (CELP):High-quality speech at very low bit rates[A].IEEE International Conference on Acoustics,Speech,and Signal Processing[C].USA:IEEE,1985.937-940.
[7] GIACOBELLO D,CHRISTENSEN M G,MURTHI M N,et al.Sparse linear prediction and its applications to speech processing[J].IEEE Transactions on Audio,Speech,and Language Processing,2012,20(5):1644-1657.
[8] 刘敬伟,王作英,肖熙.基于自回归模型的加性噪声环境稳健语音识别[J].清华大学学报(自然科学版),2006,(01):50-53. LIU J-W,WANG Z-Y,XIAO X.Autoregressive model-based robust speech recognition in additive noise environment[J].Journal of Tsinghua University(Science and Technology),2006,(01):50-53.(in Chinese)
[9] ROE D.Speech recognition with a noise-adapting codebook[A].IEEE International Conference on Acoustics,Speech,and Signal Processing[C].USA:IEEE,1987.1139-1142.
[10] EPHRAIM Y,MALAH D.Speech enhancement using a minimum mean-square error log-spectral amplitude estimator[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1985,33(2):443-445.
[11] CUI Z H,BAO C C.Linear prediction-based part-defined auto-encoder used for speech enhancement[A].IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP)[C].USA:IEEE,2019.6880-6884.
[12] 何玉文,鲍长春,夏丙寅.基于AR-HMM在线能量调整的语音增强方法[J].电子学报,2014,42(10):1991-1997. HE Yu-wen,BAO Chang-chun,XIA Bing-yin.Online energy adjustment using AR-HMM for speech enhancement[J].Acta Electronica Sinica,2014,42(10):1991-1997.(in Chinese)
[13] 孟宪波,鲍长春.基于最小控制GARCH模型的噪声估计算法[J].电子学报,2016,44(3):747-752. MENG Xian-bo,BAO Cang-chun.Noise estimate algorithm based on minima controlled GARCH model[J].Acta Electronica Sinica,2016,44(3):747-752.(in Chinese)
[14] WALKER G T.On periodicity in series of related terms[J].Proceedings of the Royal Society of London (Series A,Containing Papers of a Mathematical and Physical Character),1931,131(818):518-532.
[15] YULE G U.On a method of investigating periodicities disturbed series,with special reference to Wolfer's sunspot numbers[J].Philosophical Transactions of the Royal Society of London (Series A,Containing Papers of a Mathematical or Physical Character),1927,226(636-646):267-298.
[16] LEVINSON N.The Wiener (root mean square) error criterion in filter design and prediction[J].Journal of Mathematics and Physics,1946,25(1-4):261-278.
[17] DURBIN J.The fitting of time-series models[J].Revue de l'Institut International de Statistique,1960,28(3):233-244.
[18] SHI L M,JENSEN J R,CHRISTENSEN M G.Least 1-norm pole-zero modeling with sparse deconvolution for speech analysis[A].IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP)[C].USA:IEEE,2017.731-735.
[19] EL-JAROUDI A,MAKHOUL J.Discrete all-pole modeling[J].IEEE Transactions on Signal Processing,1991,39(2):411-423.
[20] MURTHI M N,RAO B D.All-pole modeling of speech based on the minimum variance distortionless response spectrum[J].IEEE Transactions on Speech and Audio Processing,2000,8(3):221-239.
[21] GRAY A,MARKEL J.Distance measures for speech processing[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1976,24(5):380-391.
[22] KRIZHEVSKY A,Sutskever I,Hinton G E.Imagenet classification with deep convolutional neural networks[A].Advances in Neural Information Processing Systems[C].USA:Curran Associates,Inc,2012.1097-1105.
[23] JI Y,ZHU W P,CHAMPAGNE B.Recurrent neural network-based dictionary learning for compressive speech sensing[J].Circuits,Systems,and Signal Processing,2019,38(8):3616-3643.
[24] 袁文浩,胡少东,时云龙,等.一种用于语音增强的卷积门控循环网络[J].电子学报,2020,48(7):1276-1283. YUAN Wen-hao,HU Shao-dong,SHI Yun-long,et al.A convolutional gated recurrent network for speech enhancement[J].Acta Electronica Sinica,2020,48(7):1276-1283.(in Chinese)
[25] CUI Z H,BAO C C,Nielsen J K,et al.Autoregressive parameter estimation with DNN-based pre-processing[A].IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP)[C].USA:IEEE,2020.6759-6763.
[26] KRISHNA H,WANG Y.The split Levinson algorithm is weakly stable[J].SIAM Journal on Numerical Analysis,1993,30(5):1498-1508.
[27] BUNCH J R.The weak and strong stability of algorithms in numerical linear algebra[J].Linear Algebra and Its Applications,1987,88:49-66.
[28] KLEIJN W B,RAMACHANDRAN R P,KROON P.Generalized analysis-by-synthesis coding and its application to pitch prediction[A].IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP)[C].USA:IEEE,1992.337.
[29] MICHELSANTI D,TAN Z H,SIGURDSSON S,et al.On training targets and objective functions for deep-learning-based audio-visual speech enhancement[A].IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP)[C].USA:IEEE,2019.8077-8081.
[30] CYBENKO G.The numerical stability of the Levinson-Durbin algorithm for Toeplitz systems of equations[J].SIAM Journal on Scientific and Statistical Computing,1980,1(3):303-319.
[31] PLANCHEREL M,LEFFLER M.Contribution à l' étude de la représentation d'une fonction arbitraire par des intégrales définies[J].Rendicontidel Circolo Matematico di Palermo (1884-1940),1910,30(1):289-335.
[32] GAROFOLO J S,LAMEL L F,FISHER W M,et al.DARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM NIST speech disc 1-1.1[J].STIN,1993,93:27403.
[33] PANAYOTOV V,GUOGUO C,DANIEL P,et al.Librispeech:An ASR corpus based on public domain audio books[A].IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP)[C].USA:IEEE,2015.5206-5210.
[34] VARGA A,STEENEKEN H J M.Assessment for automatic speech recognition:Ⅱ.NOISEX-92:A database and an experiment to study the effect of additive noise on speech recognition systems[J].Speech Communication,1993,12(3):247-251.
[35] THIEMANN J,NOBUTAKA I,EMMANUEL V.The diverse environments multi-channel acoustic noise database (DEMAND):A database of multichannel environmental noise recordings[J].The Journal of the Acoustical Society of America,2013,133(5):3591.
[36] BARKER J,MARXER R,VINCENT E,et al.The third ‘CHiME’ speech separation and recognition challenge:Dataset,task and baselines[A].IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)[C].USA:IEEE,2015.504-511.
[37] KAY S M.Modern Spectral Estimation:Theory and Application[M].India:Pearson Education India,1988.
[38] XU Y,DU J,DAI L R,et al.A regression approach to speech enhancement based on deep neural networks[J].IEEE/ACM Transactions on Audio,Speech,and Language Processing,2014,23(1):7-19.
[39] XU B,WANG N,CHEN T,et al.Empirical evaluation of rectified activations in convolutional network[J].arXiv Preprint,2015,arXiv:1505.00853.
[40] SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:A simple way to prevent neural networks from overfitting[J].The Journal of Machine Learning Research,2014,15(1):1929-1958.
[41] LIU L,JIANG H,HE P,et al.On the variance of the adaptive learning rate and beyond[J].arXiv Preprint,2019,arXiv:1908.03265.
[42] PASZKE A,GROSS S,MASSA F,et al.Pytorch:An imperative style,high-performance deep learning library[A].Conference on Neural Information Processing Systems (NIPS)[C].Vancouver,Canada:NIPS,2019.8026-8037. |