3 citations · 7 across the 6 of their papers we have counts for
4 papers · 1 filter
InfantNet: A Deep Neural Network for Analyzing Infant Vocalizations
Mohammad K. Ebrahimpour, Sara Schneider, David C. Noelle +1
Acoustic analyses of infant vocalizations are valuable for research on speech development as well as applications in sound classification. Previous studies have focused on measures…
End-to-End Auditory Object Recognition via Inception Nucleus
Mohammad K. Ebrahimpour, Timothy Shea, Andreea Danielescu +2
Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum. More recently, end-to…
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention
Mohammad K. Ebrahimpour, Jiayun Li, Yen-Yun Yu +4
Deep Convolutional Neural Networks (CNNs) have been repeatedly proven to perform well on image classification tasks. Object detection methods, however, are still in need of signifi…
WW-Nets: Dual Neural Networks for Object Detection
Mohammad K. Ebrahimpour, J. Ben Falandays, Samuel Spevack +2
We propose a new deep convolutional neural network framework that uses object location knowledge implicit in network connection weights to guide selective attention in object detec…