The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Speech enhancement based on deep denoising autoencoder
Abstract
We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using only clean speech. In this study, we further introduce an explicit denoising process in learning the DAE. In training the DAE, we still adopt greedy layer-wised pretraining plus fine tuning strategy. In pretraining, each layer is trained as a one hidden layer neural autoencoder (AE) using noisy-clean speech pairs as input and output (or transformed noisy-clean speech pairs by preceding AEs). Fine tuning was done by stacking all AEs with pretrained parameters for initialization. The trained DAE is used as a filter for speech estimation when noisy speech is given. Speech enhancement experiments were done to examine the performance of the trained denoising DAE. Noise reduction, speech distortion, and perceptual evaluation of speech quality (PESQ) criteria are used in the performance evaluations. Experimental results show that adding depth of the DAE consistently increase the performance when a large training data set is given. In addition, compared with a minimum mean square error based speech enhancement algorithm, our proposed denoising DAE provided superior performance on the three objective evaluations.
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Cites
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
Speech enhancement theory and practice
Loizou, P. C. (2007) · Cited by 1,823
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
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(2007) · Cited by 849
Binary coding of speech spectrograms using a deep auto-encoder
Deng, L., Seltzer, M. L., Yu, D., Acero, A., Mohamed, A.-R., & Hinton, G. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-487 · Cited by 375
Recurrent neural networks for noise reduction in robust ASR
Maas, A. L., Le, Q. V., O'Neil, T. M., Vinyals, O., Nguyen, P., & Ng, A. Y. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-6 · Cited by 334
Cited by
Deep Learning for Audio Signal Processing
Purwins, H., Li, B., Virtanen, T., Schluter, J., Chang, S.-Y., & Sainath, T. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2908700 · Cited by 855
A review of deep learning techniques for speech processing
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389
Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders
Chiang, H.-T., Hsieh, Y.-Y., Fu, S.-W., Hung, K.-H., Tsao, Y., & Chien, S.-Y. (2019) · IEEE Access · doi:10.1109/access.2019.2912036 · Cited by 363
Self-citationTan, K., & Wang, D. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2019.2955276 · Cited by 348
Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach
Fang, S.-H., Tsao, Y., Hsiao, M.-J., Chen, J.-Y., Lai, Y.-H., Lin, F.-C., & Wang, C.-T. (2019) · Journal of Voice · doi:10.1016/j.jvoice.2018.02.003 · Cited by 278
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Metadata
- Authors
- Lu, X., Tsao, Y., Matsuda, S., & Hori, C.
- Venue
- Interspeech 2013
- Publisher
- ISCA
- Published
- 2013-08-25
- Pages
- 436-440
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2405774341
- Cited by
- 834
- References
- 12
- FWCI
- 20.86
- Type
- conference paper
- Language
- en
- Open access
- closed
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