26 citations · 64 across the 7 of their papers we have counts for
7 papers
DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets
Ali Mahdi, Jun Qin
A deep feature based saliency model (DeepFeat) is developed to leverage the understanding of the prediction of human fixations. Traditional saliency models often predict the human…
Investigations of Auditory Filters Based Excitation Patterns for Assessment of Noise Induced Hearing Loss
Wisam Subhi Al-Dayyeni, Pengfei Sun, Jun Qin
Noise induced hearing loss (NIHL) as one of major avoidable occupational related health issues has been studied for decades. To assess NIHL, the excitation pattern (EP) has been co…
Neural networks based EEG-Speech Models
Pengfei Sun, Jun Qin
In this paper, we propose an end-to-end neural network (NN) based EEG-speech (NES) modeling framework, in which three network structures are developed to map imagined EEG signals t…
Enhanced Factored Three-Way Restricted Boltzmann Machines for Speech Detection
Pengfei Sun, Jun Qin
In this letter, we propose enhanced factored three way restricted Boltzmann machines (EFTW-RBMs) for speech detection. The proposed model incorporates conditional feature learning…
Speech Enhancement via Two-Stage Dual Tree Complex Wavelet Packet Transform with a Speech Presence Probability Estimator
Pengfei Sun, Jun Qin
In this paper, a two-stage dual tree complex wavelet packet transform (DTCWPT) based speech enhancement algorithm has been proposed, in which a speech presence probability (SPP) es…
Low Rank and Sparsity Analysis Applied to Speech Enhancement via Online Estimated Dictionary
Pengfei Sun, Jun Qin
We propose an online estimated dictionary based single channel speech enhancement algorithm, which focuses on low rank and sparse matrix decomposition. In this proposed algorithm,…