Modular dynamic deep denoising autoencoder for speech enhancement - Safari, Razieh; Ahadi, Seyed Mohammad; Seyedin, Sanaz (2017 7th International Conference on Computer and Knowledge Engineering (ICCKE), 2017)
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.