3 papers
eess.AS2020
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source Separation
Sunwoo Kim, Haici Yang, Minje Kim
Speech enhancement tasks have seen significant improvements with the advance of deep learning technology, but with the cost of increased computational complexity. In this study, we…
eess.AS2019
Nearest Neighbor Search-Based Bitwise Source Separation Using Discriminant Winner-Take-All Hashing
Sunwoo Kim, Minje Kim
We propose an iteration-free source separation algorithm based on Winner-Take-All (WTA) hash codes, which is a faster, yet accurate alternative to a complex machine learning model…
eess.AS2019
Incremental Binarization On Recurrent Neural Networks For Single-Channel Source Separation
Sunwoo Kim, Mrinmoy Maity, Minje Kim
This paper proposes a Bitwise Gated Recurrent Unit (BGRU) network for the single-channel source separation task. Recurrent Neural Networks (RNN) require several sets of weights wit…