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20192023
most citedReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2023★ 1 cited

A Demand-Driven Perspective on Generative Audio AI

Sangshin Oh, Minsung Kang, Hyeongi Moon +2

To achieve successful deployment of AI research, it is crucial to understand the demands of the industry. In this paper, we present the results of a survey conducted with professio…

eess.AS2023

FALL-E: A Foley Sound Synthesis Model and Strategies

Minsung Kang, Sangshin Oh, Hyeongi Moon +2

This paper introduces FALL-E, a foley synthesis system and its training/inference strategies. The FALL-E model employs a cascaded approach comprising low-resolution spectrogram gen…

cs.SD2022★ 1 cited

A Proposal for Foley Sound Synthesis Challenge

Keunwoo Choi, Sangshin Oh, Minsung Kang +1

"Foley" refers to sound effects that are added to multimedia during post-production to enhance its perceived acoustic properties, e.g., by simulating the sounds of footsteps, ambie…

eess.AS2022★ 1 cited

ReCAB-VAE: Gumbel-Softmax Variational Inference Based on Analytic Divergence

Sangshin Oh, Seyun Um, Hong-Goo Kang

The Gumbel-softmax distribution, or Concrete distribution, is often used to relax the discrete characteristics of a categorical distribution and enable back-propagation through dif…

eess.AS2019

Emotional speech synthesis with rich and granularized control

Se-Yun Um, Sangshin Oh, Kyungguen Byun +3

This paper proposes an effective emotion control method for an end-to-end text-to-speech (TTS) system. To flexibly control the distinct characteristic of a target emotion category,…