14 citations · 19 across the 9 of their papers we have counts for
9 papers
Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning
Emad M. Grais, Xiaoya Wang, Jie Wang +6
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation…
Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation
Emad M. Grais, Fei Zhao, Mark D. Plumbley
Deep neural networks with convolutional layers usually process the entire spectrogram of an audio signal with the same time-frequency resolutions, number of filters, and dimensiona…
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks
Emad M. Grais, Hagen Wierstorf, Dominic Ward +2
Current performance evaluation for audio source separation depends on comparing the processed or separated signals with reference signals. Therefore, common performance evaluation…
Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders
Emad M. Grais, Dominic Ward, Mark D. Plumbley
Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is…
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation
Emad M. Grais, Hagen Wierstorf, Dominic Ward +1
In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF). Convolutional layers with a large RF capture global i…
Single Channel Audio Source Separation using Convolutional Denoising Autoencoders
Emad M. Grais, Mark D. Plumbley
Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDA…
Discriminative Enhancement for Single Channel Audio Source Separation using Deep Neural Networks
Emad M. Grais, Gerard Roma, Andrew J. R. Simpson +1
The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To…
Deep neural networks for single channel source separation
Emad M. Grais, Mehmet Umut Sen, Hakan Erdogan
In this paper, a novel approach for single channel source separation (SCSS) using a deep neural network (DNN) architecture is introduced. Unlike previous studies in which DNN and o…
Source Separation using Regularized NMF with MMSE Estimates under GMM Priors with Online Learning for The Uncertainties
Emad M. Grais, Hakan Erdogan
We propose a new method to enforce priors on the solution of the nonnegative matrix factorization (NMF). The proposed algorithm can be used for denoising or single-channel source s…