30 citations · 42 across the 18 of their papers we have counts for
27 papers
The Potential of Neural Speech Synthesis-based Data Augmentation for Personalized Speech Enhancement
Anastasia Kuznetsova, Aswin Sivaraman, Minje Kim
With the advances in deep learning, speech enhancement systems benefited from large neural network architectures and achieved state-of-the-art quality. However, speaker-agnostic me…
Neural Feature Predictor and Discriminative Residual Coding for Low-Bitrate Speech Coding
Haici Yang, Wootaek Lim, Minje Kim
Low and ultra-low-bitrate neural speech coding achieves unprecedented coding gain by generating speech signals from compact speech features. This paper introduces additional coding…
Upmixing via style transfer: a variational autoencoder for disentangling spatial images and musical content
Haici Yang, Sanna Wager, Spencer Russell +3
In the stereo-to-multichannel upmixing problem for music, one of the main tasks is to set the directionality of the instrument sources in the multichannel rendering results. In thi…
SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation
Darius Petermann, Minje Kim
With the recent advancements of data driven approaches using deep neural networks, music source separation has been formulated as an instrument-specific supervised problem. While e…
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…