Unsupervised feature learning for audio classification using convolutional deep belief networks
暂无分享,去创建一个
Honglak Lee | Andrew Y. Ng | Peter T. Pham | Yan Largman | A. Ng | Honglak Lee | Peter T. Pham | Yan Largman
[1] 直樹 武川,et al. Regularization , 2019, Encyclopedia of Continuum Mechanics.
[2] Douglas A. Reynolds,et al. Speaker identification and verification using Gaussian mixture speaker models , 1995, Speech Commun..
[3] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[4] Pedro J. Moreno,et al. On the use of support vector machines for phonetic classification , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[5] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[6] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[7] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[8] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[9] Michael S. Lewicki,et al. Efficient auditory coding , 2006, Nature.
[10] Lawrence K. Saul,et al. Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[11] Daniel Jurafsky,et al. Regularization, adaptation, and non-independent features improve hidden conditional random fields for phone classification , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).
[12] Roger B. Grosse,et al. Shift-Invariance Sparse Coding for Audio Classification , 2007, UAI.
[13] Honglak Lee,et al. Sparse deep belief net model for visual area V2 , 2007, NIPS.
[14] Dan Klein,et al. Learning Structured Models for Phone Recognition , 2007, EMNLP.
[15] Rajat Raina,et al. Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.
[16] Yoshua Bengio,et al. An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.
[17] Mohammad Norouzi,et al. Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[19] Dong Yu,et al. Hidden conditional random field with distribution constraints for phone classification , 2009, INTERSPEECH.