Speech Enhancement Using Convolutional Denoising Autoencoder

@article{Shahriyar2019SpeechEU,
  title={Speech Enhancement Using Convolutional Denoising Autoencoder},
  author={Shaikh Akib Shahriyar and M. A. H. Akhand and Nazmul Siddique and Tetsuya Shimamura},
  journal={2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)},
  year={2019},
  pages={1-5}
}
  • S. A. Shahriyar, M. Akhand, T. Shimamura
  • Published 1 February 2019
  • Computer Science
  • 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)
Speech signals are complex in nature with respect to other forms of communication media such as text or image. Different forms of noises (e.g., additive noise, channel noise, babble noise) interfere with the speech signals and drastically hamper the quality of the speech in the noisy speech signals. Enhancement of speech signals is a daunting task considering multiple forms of noises while denoising speech signals. Certain analog noise eliminator models have been studied over the years for this… 

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References

SHOWING 1-10 OF 19 REFERENCES
Speech enhancement with weighted denoising auto-encoder
TLDR
A novel speech enhancement method with Weighted Denoising Auto-encoder (WDA) is proposed, which could achieve similar amount of noise reduction in both white and colored noise, and the distortion on the level of speech signal is smaller.
SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement
TLDR
CNN with the two proposed SNR-aware algorithms outperform the deep neural network counterpart in terms of standardized objective evaluations when using the same number of layers and nodes, suggesting their promising generalization capability for real-world applications.
An Experimental Study on Speech Enhancement Based on Deep Neural Networks
TLDR
This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture that tends to achieve significant improvements in terms of various objective quality measures.
A Regression Approach to Speech Enhancement Based on Deep Neural Networks
TLDR
The proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general, and is effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.
Complex recurrent neural networks for denoising speech signals
  • K. Osako, Rita Singh, B. Raj
  • Computer Science, Engineering
    2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
  • 2015
TLDR
Noise reduction experiments on noisy speech, both with digitally added synthetic noise and real car noise, show that the proposed algorithm can recover much of the degradation caused by the noise.
A signal subspace approach for speech enhancement
TLDR
The popular spectral subtraction speech enhancement approach is shown to be a signal subspace approach which is optimal in an asymptotic (large sample) linear minimum mean square error sense, assuming the signal and noise are stationary.
A Fully Convolutional Neural Network for Speech Enhancement
TLDR
The proposed network, Redundant Convolutional Encoder Decoder (R-CED), demonstrates that a convolutional network can be 12 times smaller than a recurrent network and yet achieves better performance, which shows its applicability for an embedded system: the hearing aids.
Speech Enhancement Using a-Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator
TLDR
This paper derives a minimum mean-square error STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables, which results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise.
Learning spectral mapping for speech dereverberation
  • Kun Han, Yuxuan Wang, Deliang Wang
  • Physics, Computer Science
    2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2014
TLDR
It is demonstrated that distortion caused by reverberation is substantially attenuated by the DNN whose outputs can be resynthesized to the dereverebrated speech signal.
Enhancement and bandwidth compression of noisy speech
TLDR
An overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise is provided to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.
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