I3E Technologies, #23/A, 2nd Floor SKS Complex, Opp. Bus Stand, Karur-639 001. IEEE PROJECTS AVAILABLE, NON-IEEE PROJECTS AVAILABLE, APPLICATION BASED PROJECTS AVAILABLE, MINI-PROJECTS AVAILABLE. Mobile : 99436 99916, 99436 99926, 99436 99936 Mail ID: i3eprojectskarur@gmail.com Web: www.i3etechnologies.com, www.i3eprojects.com

1. A REGRESSION APPROACH TO SPEECH ENHANCEMENT
BASED ON DEEP NEURAL NETWORKS
ABSTRACT
In contrast to the conventional minimum mean square error (MMSE)-based noise
reduction techniques, we propose a supervised method to enhance speech by means of finding a
mapping function between noisy and clean speech signals based on deep neural networks
(DNNs). In order to be able to handle a wide range of additive noises in real-world situations, a
large training set that encompasses many possible combinations of speech and noise types, is
first designed. A DNN architecture is then employed as a nonlinear regression function to ensure
a powerful modeling capability. Several chniques have also been proposed to improve the DNN-
based speech enhancement system, including global variance equalization to alleviate the over-
smoothing problem of the regression model, and the dropout and noise-aware training strategies
to further improve the generalization capability of DNNs to unseen noise conditions.
Experimental results demonstrate that the proposed framework can achieve significant
improvements in both objective and subjective measures over the conventional MMSE based
technique. It is also interesting to observe that the proposed DNN approach can well suppress
highly nonstationary noise, which is tough to handle in general. Furthermore, the resulting DNN
model, trained with artificial synthesized data, is also effective in dealing with noisy peech data
recorded in real-world scenarios without the generation of the nnoyingmusical artifact commonly
observed in conventional enhancement methods.