Single-Channel Speech Enhancement Based on Frequency Domain ALE

In the present paper, a new single-channel speech enhancement system is proposed. The proposed system is based on frequency domain adaptive line enhancer; therefore, it is advantageous to non-stationary environments. Also, frequency domain decorrelation parameters are introduced and then adjusted independently. The performance of the proposed system is examined through computer simulations. The effectiveness of the proposed system is confirmed through computer simulations

Digital signal processing algorithms and structures for adaptive line enhancing

Imperial Users onl

Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models

Single-channel deep speech enhancement approaches often estimate a single multiplicative mask to extract clean speech without a measure of its accuracy. Instead, in this work, we propose to quantify the uncertainty associated with clean speech estimates in neural network-based speech enhancement. Predictive uncertainty is typically categorized into aleatoric uncertainty and epistemic uncertainty. The former accounts for the inherent uncertainty in data and the latter corresponds to the model uncertainty. Aiming for robust clean speech estimation and efficient predictive uncertainty quantification, we propose to integrate statistical complex Gaussian mixture models (CGMMs) into a deep speech enhancement framework. More specifically, we model the dependency between input and output stochastically by means of a conditional probability density and train a neural network to map the noisy input to the full posterior distribution of clean speech, modeled as a mixture of multiple complex Gaussian components. Experimental results on different datasets show that the proposed algorithm effectively captures predictive uncertainty and that combining powerful statistical models and deep learning also delivers a superior speech enhancement performance.Comment: 5 pages, 4 figure

A Subband Hybrid Beamforming for In-car Speech Enhancement

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Theory, design and application of gradient adaptive lattice filters

SIGLELD:D48933/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Implementation and evaluation of a low complexity microphone array for speaker recognition

Includes bibliographical references (leaves 83-86).This thesis discusses the application of a microphone array employing a noise canceling beamforming technique for improving the robustness of speaker recognition systems in a diffuse noise field