Phase and reverberation aware DNN for distant-talking speech enhancement
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Jianwu Dang | Longbiao Wang | Masahiro Iwahashi | Seiichi Nakagawa | Khomdet Phapatanaburi | Zeyan Oo | J. Dang | Longbiao Wang | S. Nakagawa | M. Iwahashi | Khomdet Phapatanaburi | Zeyan Oo
[1] Sridha Sridharan,et al. JFA based speaker recognition using delta-phase and MFCC features , 2012 .
[2] Jun Du,et al. An Experimental Study on Speech Enhancement Based on Deep Neural Networks , 2014, IEEE Signal Processing Letters.
[3] Wang Longbiao,et al. Investigation of DNN Based Distant-Talking Speech Enhancement , 2015 .
[4] Yongqiang Wang,et al. An investigation of deep neural networks for noise robust speech recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[5] Yariv Ephraim,et al. A signal subspace approach for speech enhancement , 1995, IEEE Trans. Speech Audio Process..
[6] Seiichi Nakagawa,et al. PAPER Special Section on Processing Natural Speech Variability for Improved Verbal Human-Computer Interaction Speaker Recognition by Combining MFCC and Phase Information in Noisy Conditions , 2010 .
[7] Jacob Benesty,et al. Speech Enhancement , 2010 .
[8] Longbiao Wang,et al. Speaker Identification and Verification by Combining MFCC and Phase Information , 2009, IEEE Transactions on Audio, Speech, and Language Processing.
[9] Mohamed-Slim Alouini,et al. Instantly decodable network coding for real-time device-to-device communications , 2016, EURASIP J. Adv. Signal Process..
[10] Jun Du,et al. Dynamic noise aware training for speech enhancement based on deep neural networks , 2014, INTERSPEECH.
[11] Biing-Hwang Juang,et al. Blind speech dereverberation with multi-channel linear prediction based on short time fourier transform representation , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
[12] Tomohiro Nakatani,et al. The reverb challenge: A common evaluation framework for dereverberation and recognition of reverberant speech , 2013, 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.
[13] Bo Ren,et al. Environment-dependent denoising autoencoder for distant-talking speech recognition , 2015, EURASIP Journal on Advances in Signal Processing.
[14] Ephraim. Speech enhancement using a minimum mean square error short-time spectral amplitude estimator , 1984 .
[15] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[16] Birger Kollmeier,et al. SNR estimation based on amplitude modulation analysis with applications to noise suppression , 2003, IEEE Trans. Speech Audio Process..
[17] Tomohiro Nakatani,et al. Suppression of Late Reverberation Effect on Speech Signal Using Long-Term Multiple-step Linear Prediction , 2009, IEEE Transactions on Audio, Speech, and Language Processing.
[18] Xiaoli Ma,et al. Eigenvector-based initial ranging process for OFDMA uplink systems , 2015, EURASIP Journal on Advances in Signal Processing.
[19] Yu Tsao,et al. Speech enhancement based on deep denoising autoencoder , 2013, INTERSPEECH.
[20] J. Foote,et al. WSJCAM0: A BRITISH ENGLISH SPEECH CORPUS FOR LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION , 1995 .
[21] Steve Renals,et al. WSJCAMO: a British English speech corpus for large vocabulary continuous speech recognition , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[22] Haizhou Li,et al. Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation , 2016, EURASIP J. Adv. Signal Process..
[23] Longbiao Wang,et al. Relative phase information for detecting human speech and spoofed speech , 2015, INTERSPEECH.
[24] Rajesh M. Hegde,et al. Significance of the Modified Group Delay Feature in Speech Recognition , 2007, IEEE Transactions on Audio, Speech, and Language Processing.
[25] S. Boll,et al. Suppression of acoustic noise in speech using spectral subtraction , 1979 .