79 citations · 114 across the 4 of their papers we have counts for
5 papers
Modeling Musical Onset Probabilities via Neural Distribution Learning
Jaesung Huh, Egil Martinsson, Adrian Kim +1
Musical onset detection can be formulated as a time-to-event (TTE) or time-since-event (TSE) prediction task by defining music as a sequence of onset events. Here we propose a nove…
Phase-aware Speech Enhancement with Deep Complex U-Net
Hyeong-Seok Choi, Jang-Hyun Kim, Jaesung Huh +3
Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction.…
Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement
Jang-Hyun Kim, Jaejun Yoo, Sanghyuk Chun +2
We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task.…
CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
Jinwoong Kim, Minkyu Kim, Heungseok Park +6
Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance. Here we propose a novel cloud-based HyperOpt (C…
Automatic Music Highlight Extraction using Convolutional Recurrent Attention Networks
Jung-Woo Ha, Adrian Kim, Chanju Kim +2
Music highlights are valuable contents for music services. Most methods focused on low-level signal features. We propose a method for extracting highlights using high-level feature…