255 citations · 1.2k across the 38 of their papers we have counts for
4 papers · 1 filter
Deep Clustering and Conventional Networks for Music Separation: Stronger Together
Yi Luo, Zhuo Chen, John R. Hershey +2
Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively…
Full-Capacity Unitary Recurrent Neural Networks
Scott Wisdom, Thomas Powers, John R. Hershey +2
Recurrent neural networks are powerful models for processing sequential data, but they are generally plagued by vanishing and exploding gradient problems. Unitary recurrent neural…
Single-Channel Multi-Speaker Separation using Deep Clustering
Yusuf Isik, Jonathan Le Roux, Zhuo Chen +2
Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogra…
Global-Local Face Upsampling Network
Oncel Tuzel, Yuichi Taguchi, John R. Hershey
Face hallucination, which is the task of generating a high-resolution face image from a low-resolution input image, is a well-studied problem that is useful in widespread applicati…