7 citations · 8 across the 2 of their papers we have counts for
5 papers
Deep Learning-Based Single-Ended Objective Quality Measures for Time-Scale Modified Audio
Timothy Roberts, Aaron Nicolson, Kuldip K. Paliwal
Objective evaluation of audio processed with Time-Scale Modification (TSM) is seeing a resurgence of interest. Recently, a labelled time-scaled audio dataset was used to train an o…
Deep Residual-Dense Lattice Network for Speech Enhancement
Mohammad Nikzad, Aaron Nicolson, Yongsheng Gao +3
Convolutional neural networks (CNNs) with residual links (ResNets) and causal dilated convolutional units have been the network of choice for deep learning approaches to speech enh…
Monaural Speech Enhancement Using a Multi-Branch Temporal Convolutional Network
Qiquan Zhang, Aaron Nicolson, Mingjiang Wang +2
Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited b…
Sum-Product Networks for Robust Automatic Speaker Identification
Aaron Nicolson, Kuldip K. Paliwal
We introduce sum-product networks (SPNs) for robust speech processing through a simple robust automatic speaker identification (ASI) task. SPNs are deep probabilistic graphical mod…
Deep Xi as a Front-End for Robust Automatic Speech Recognition
Aaron Nicolson, Kuldip K. Paliwal
Current front-ends for robust automatic speech recognition(ASR) include masking- and mapping-based deep learning approaches to speech enhancement. A recently proposed deep learning…