15 citations · 52 across the 21 of their papers we have counts for
21 papers
Exploiting Time-Frequency Conformers for Music Audio Enhancement
Yunkee Chae, Junghyun Koo, Sungho Lee +1
With the proliferation of video platforms on the internet, recording musical performances by mobile devices has become commonplace. However, these recordings often suffer from degr…
Music De-limiter Networks via Sample-wise Gain Inversion
Chang-Bin Jeon, Kyogu Lee
The loudness war, an ongoing phenomenon in the music industry characterized by the increasing final loudness of music while reducing its dynamic range, has been a controversial top…
Towards a New Interface for Music Listening: A User Experience Study on YouTube
Ahyeon Choi, Eunsik Shin, Haesun Joung +2
In light of the enduring success of music streaming services, it is noteworthy that an increasing number of users are positively gravitating toward YouTube as their preferred platf…
Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data
Junghyun Koo, Yunkee Chae, Chang-Bin Jeon +1
Music source separation (MSS) faces challenges due to the limited availability of correctly-labeled individual instrument tracks. With the push to acquire larger datasets to improv…
Debiased Automatic Speech Recognition for Dysarthric Speech via Sample Reweighting with Sample Affinity Test
Eungbeom Kim, Yunkee Chae, Jaeheon Sim +1
Automatic speech recognition systems based on deep learning are mainly trained under empirical risk minimization (ERM). Since ERM utilizes the averaged performance on the data samp…
Blind Estimation of Audio Processing Graph
Sungho Lee, Jaehyun Park, Seungryeol Paik +1
Musicians and audio engineers sculpt and transform their sounds by connecting multiple processors, forming an audio processing graph. However, most deep-learning methods overlook t…
Show Me the Instruments: Musical Instrument Retrieval from Mixture Audio
Kyungsu Kim, Minju Park, Haesun Joung +4
As digital music production has become mainstream, the selection of appropriate virtual instruments plays a crucial role in determining the quality of music. To search the musical…
MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
Chang-Bin Jeon, Hyeongi Moon, Keunwoo Choi +2
Separation of multiple singing voices into each voice is a rarely studied area in music source separation research. The absence of a benchmark dataset has hindered its progress. In…
Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations
Yoori Oh, Juheon Lee, Yoseob Han +1
Recent text-to-speech models have reached the level of generating natural speech similar to what humans say. But there still have limitations in terms of expressiveness. The existi…
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects
Junghyun Koo, Marco A. Martínez-Ramírez, Wei-Hsiang Liao +3
We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song. This is achieved with an encoder pre-…
Pop2Piano : Pop Audio-based Piano Cover Generation
Jongho Choi, Kyogu Lee
Piano covers of pop music are enjoyed by many people. However, the task of automatically generating piano covers of pop music is still understudied. This is partly due to the lack…
Neural Fourier Shift for Binaural Speech Rendering
Jin Woo Lee, Kyogu Lee
We present a neural network for rendering binaural speech from given monaural audio, position, and orientation of the source. Most of the previous works have focused on synthesizin…
Exploring Train and Test-Time Augmentations for Audio-Language Learning
Eungbeom Kim, Jinhee Kim, Yoori Oh +5
In this paper, we aim to unveil the impact of data augmentation in audio-language multi-modal learning, which has not been explored despite its importance. We explore various augme…
Sketching the Expression: Flexible Rendering of Expressive Piano Performance with Self-Supervised Learning
Seungyeon Rhyu, Sarah Kim, Kyogu Lee
We propose a system for rendering a symbolic piano performance with flexible musical expression. It is necessary to actively control musical expression for creating a new music per…
Towards robust music source separation on loud commercial music
Chang-Bin Jeon, Kyogu Lee
Nowadays, commercial music has extreme loudness and heavily compressed dynamic range compared to the past. Yet, in music source separation, these characteristics have not been thor…
Expressive Singing Synthesis Using Local Style Token and Dual-path Pitch Encoder
Juheon Lee, Hyeong-Seok Choi, Kyogu Lee
This paper proposes a controllable singing voice synthesis system capable of generating expressive singing voice with two novel methodologies. First, a local style token module, wh…
Representation Selective Self-distillation and wav2vec 2.0 Feature Exploration for Spoof-aware Speaker Verification
Jin Woo Lee, Eungbeom Kim, Junghyun Koo +1
Text-to-speech and voice conversion studies are constantly improving to the extent where they can produce synthetic speech almost indistinguishable from bona fide human speech. In…
Global HRTF Interpolation via Learned Affine Transformation of Hyper-conditioned Features
Jin Woo Lee, Sungho Lee, Kyogu Lee
Estimating Head-Related Transfer Functions (HRTFs) of arbitrary source points is essential in immersive binaural audio rendering. Computing each individual's HRTFs is challenging,…
End-to-end Music Remastering System Using Self-supervised and Adversarial Training
Junghyun Koo, Seungryeol Paik, Kyogu Lee
Mastering is an essential step in music production, but it is also a challenging task that has to go through the hands of experienced audio engineers, where they adjust tone, space…
Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations
Hyeong-Seok Choi, Juheon Lee, Wansoo Kim +3
We present a neural analysis and synthesis (NANSY) framework that can manipulate voice, pitch, and speed of an arbitrary speech signal. Most of the previous works have focused on u…
Cross-domain Semi-Supervised Audio Event Classification Using Contrastive Regularization
Donmoon Lee, Kyogu Lee
In this study, we proposed a novel semi-supervised training method that uses unlabeled data with a class distribution that is completely different from the target data or data with…