Speech enhancement based on deep denoising autoencoder

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2013, Pages 436-440

  Lu, Xugang ^a   Tsao, Yu ^b   Matsuda, Shigeki ^a   Hori, Chiori ^a  

^a NATIONAL INSTITUTE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY   (Japan)

^b RESEARCH CENTER FOR INFORMATION TECHNOLOGY INNOVATION   (Taiwan)

Author keywords

Autoencoder; Deep autoencoder learning; Noise reduction; Speech enhancement

Indexed keywords

LEARNING SYSTEMS; NOISE ABATEMENT; QUALITY CONTROL;

AUTO ENCODERS; INPUT AND OUTPUTS; MINIMUM MEAN SQUARE ERRORS; OBJECTIVE EVALUATION; PERCEPTUAL EVALUATION OF SPEECH QUALITIES; SPEECH DISTORTION; SPEECH ENHANCEMENT ALGORITHM; TRAINING DATA SETS;

SPEECH ENHANCEMENT;

EID: 84906262433     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Loizou, P. C., Speech Enhancement: Theory and Practice, CRC Press, 2007.
2. Lu, X., Unoki, M., Matsuda, S., Hori, C., Kashioka, H., Controlling tradeoff between approximation accuracy and complexity of a smooth function in a reproducing kernel Hilbert space for noise reduction, IEEE Trans. on Signal Processing, 61 (3): 601-610, 2013.
3. Bengio, Y., Learning deep architectures for AI, Foundations and Trends in Machine Learning, 2(1): 1-127, 2009.
4. Hinton, G. E., and Salakhutdinov, R., Reducing the Dimensionality of Data with Neural Networks, Science, 313: 504-507, 2006.
5. Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H., Greedy layer-wise training of deep networks, Advances in Neural Information Processing Systems, 19: 153-160, MIT Press, Cambridge, 2007.
6. Ranzato, M. A., Huang, F. J., Boureau, Y. L., LeCun, Y., Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition, IEEE conference on Computer Vision and Pattern Recognition, 1-8, 2007.
7. Lu, X., Matsuda, S., Hori, C., Kashioka, H., Speech restoration based on deep learning autoencoder with layer-wised learning, INTERSPEECH, Portland, Oregon, Sept., 2012.
8. Dahl, G., Yu, D., Deng, L., Acero, A., Context-dependent pretrained deep neural networks for large vocabulary speech recognition, IEEE Transactions on Audio, Speech, and Language Processing, 20 (1): 30-42, 2011.
9. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P., Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, Journal of Machine Learning Research, 11(Dec): 3371-3408, 2010.
10. Maas, A, Le, Q., O'Neil, T., Vinyals, O., Nguyen, P., Ng, A, Recurrent Neural Networks for Noise Reduction in Robust ASR, Interspeech 2012, Portland, 2012.
11. Schmidt, M., Van Den Berg, E., Friedl, M. P., Murphy, K., Optimizing costly functions with simple constraints: A limitedmemory projected quasi-newton algorithm, Proc. of Conf. on Artificial Intelligence and Statistics, 456-463, 2009.
12. Deng, L., Seltzer, M., Yu, D., Acero, A., Mohamed, A., Hinton, A., Binary Coding of Speech Spectrograms Using a Deep Autoencoder, Proc. of Interspeech, 1692-1695, 2010.
13. Cohen, I., Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging, IEEE Trans. Speech Audio Process. 11 (5): 466-475, 2003.
14. Lee, H., Ekanadham, C., and Ng, A. Y., Sparse deep belief net model for visual area V2, Advances in Neural Information Processing Systems (NIPS), 20: 873-880, 2008.