479 citations · 827 across the 35 of their papers we have counts for
8 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…
HARP-Net: Hyper-Autoencoded Reconstruction Propagation for Scalable Neural Audio Coding
Darius Petermann, Seungkwon Beack, Minje Kim
An autoencoder-based codec employs quantization to turn its bottleneck layer activation into bitstrings, a process that hinders information flow between the encoder and decoder par…
Don't Separate, Learn to Remix: End-to-End Neural Remixing with Joint Optimization
Haici Yang, Shivani Firodiya, Nicholas J. Bryan +1
The task of manipulating the level and/or effects of individual instruments to recompose a mixture of recordings, or remixing, is common across a variety of applications such as mu…
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…
Zero-Shot Personalized Speech Enhancement through Speaker-Informed Model Selection
Aswin Sivaraman, Minje Kim
This paper presents a novel zero-shot learning approach towards personalized speech enhancement through the use of a sparsely active ensemble model. Optimizing speech denoising sys…
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…
Efficient Personalized Speech Enhancement through Self-Supervised Learning
Aswin Sivaraman, Minje Kim
This work presents self-supervised learning methods for developing monaural speaker-specific (i.e., personalized) speech enhancement models. While generalist models must broadly ad…
Scalable and Efficient Neural Speech Coding: A Hybrid Design
Kai Zhen, Jongmo Sung, Mi Suk Lee +2
We present a scalable and efficient neural waveform coding system for speech compression. We formulate the speech coding problem as an autoencoding task, where a convolutional neur…