Modular dynamic deep denoising autoencoder for speech enhancement

Deep Denoising Autoencoder (DDAE) is an effective method for noise reduction and speech enhancement. However, a single DDAE with a fixed number of frames for neural network input cannot extract contextual information sufficiently. It has also less generalization in unknown SNRs (signal-to-noise-rati...

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Bibliographic Details
Published in:	2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) pp. 254 - 259
Main Authors:	Safari, Razieh, Ahadi, Seyed Mohammad, Seyedin, Sanaz
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.10.2017
Subjects:	Deep Denoising Autoencoder (DDAE) Improved Context Pattern Modular Dynamic Deep Denoising Autoencoder (MD-DDAE) Neural networks Noise measurement Noise reduction Signal to noise ratio Speech Speech enhancement Training Unseen Signal to Noise Ratio
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Summary:	Deep Denoising Autoencoder (DDAE) is an effective method for noise reduction and speech enhancement. However, a single DDAE with a fixed number of frames for neural network input cannot extract contextual information sufficiently. It has also less generalization in unknown SNRs (signal-to-noise-ratio) and the enhanced output has some residual noise. In this paper, we use a modular model in which three DDAEs with different window lengths are stacked. Experimental results showes that our proposed architecture, namely modular dynamic deep denoising autoencoder (MD-DDAE) provides superior performance in comparison with the traditional DDAE models in different noisy conditions.
DOI:	10.1109/ICCKE.2017.8167886