Noisy Speech Enhancement by Restoration of DFT Components Using Neural Network

SPEECH ENHANCEMENT USING TRANSIENT SPEECH COMPONENTS by

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

Enhancement of noisy speech by using improved global soft decision

We propose a novel speech enhancement algorithm, termed improved global soft decision (IGSD). IGSD is a unified framework for global soft decision on speech absence/presence, noise spectrum estimation, spectral gain modification based on Ephraim-Malah noise suppression. In IGSD, speech absence probability (SAP) is the most important factor, and we propose an efficient and novel SAP estimation i...

Noisy speech enhancement using discrete cosine transform

This paper illustrates the advantages of using the Discrete Cosine Transform (DCT) as compared to the standard Discrete Fourier Transform (DFT) for the purpose of removing noise embedded in a speech signal. The derivation of the Minimum Mean Square Error (MMSE) ®lter based on the statistical modelling of the DCT coecients is shown. Also shown is the derivation of an over-attenuation factor bas...

Speech Enhancement in the Dft Domain Using Laplacian Speech Priors

In this paper we consider optimal estimators for speech enhancement in the Discrete Fourier Transform (DFT) domain. We derive an analytical solution for estimating complex DFT coefficients in the MMSE sense when the clean speech DFT coefficients are Laplacian distributed and the DFT coefficients of the noise are Gaussian or Laplacian distributed. We show that these estimators have a number of i...