313 citations · 376 across the 17 of their papers we have counts for
6 papers · 1 filter
BLOOM-Net: Blockwise Optimization for Masking Networks Toward Scalable and Efficient Speech Enhancement
Sunwoo Kim, Minje Kim
In this paper, we present a blockwise optimization method for masking-based networks (BLOOM-Net) for training scalable speech enhancement networks. Here, we design our network with…
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation
Sunwoo Kim, Minje Kim
In realistic speech enhancement settings for end-user devices, we often encounter only a few speakers and noise types that tend to reoccur in the specific acoustic environment. We…
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification
Aswin Sivaraman, Sunwoo Kim, Minje Kim
Training personalized speech enhancement models is innately a no-shot learning problem due to privacy constraints and limited access to noise-free speech from the target user. If t…
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…
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…
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…