4 citations · 5 across the 3 of their papers we have counts for
4 papers · 1 filter
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…