17 citations · 24 across the 7 of their papers we have counts for
7 papers
Contrastive Learning for Cross-modal Artist Retrieval
Andres Ferraro, Jaehun Kim, Sergio Oramas +2
Music retrieval and recommendation applications often rely on content features encoded as embeddings, which provide vector representations of items in a music dataset. Numerous com…
Let There Be Sound: Reconstructing High Quality Speech from Silent Videos
Ji-Hoon Kim, Jaehun Kim, Joon Son Chung
The goal of this work is to reconstruct high quality speech from lip motions alone, a task also known as lip-to-speech. A key challenge of lip-to-speech systems is the one-to-many…
Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music
Hyemin Ahn, Jaehun Kim, Kihyun Kim +1
This paper proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components:…
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
Jaehun Kim, Julián Urbano, Cynthia C. S. Liem +1
Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learn…
Effective Network Compression Using Simulation-Guided Iterative Pruning
Dae-Woong Jeong, Jaehun Kim, Youngseok Kim +2
Existing high-performance deep learning models require very intensive computing. For this reason, it is difficult to embed a deep learning model into a system with limited resource…
Transfer Learning of Artist Group Factors to Musical Genre Classification
Jaehun Kim, Minz Won, Xavier Serra +1
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less nois…
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
Jaehun Kim, Julián Urbano, Cynthia C. S. Liem +1
Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of…