15 citations · 24 across the 25 of their papers we have counts for
10 papers · 1 filter
Automatic Music Mixing using a Generative Model of Effect Embeddings
Eloi Moliner, Marco A. Martínez-Ramírez, Junghyun Koo +5
Music mixing involves combining individual tracks into a cohesive mixture, a task characterized by subjectivity where multiple valid solutions exist for the same input. Existing au…
Towards Blind Data Cleaning: A Case Study in Music Source Separation
Azalea Gui, Woosung Choi, Junghyun Koo +5
The performance of deep learning models for music source separation heavily depends on training data quality. However, datasets are often corrupted by difficult-to-detect artifacts…
Fx-Encoder++: Extracting Instrument-Wise Audio Effects Representations from Mixtures
Yen-Tung Yeh, Junghyun Koo, Marco A. Martínez-Ramírez +3
General-purpose audio representations have proven effective across diverse music information retrieval applications, yet their utility in intelligent music production remains limit…
ITO-Master: Inference-Time Optimization for Audio Effects Modeling of Music Mastering Processors
Junghyun Koo, Marco A. Martínez-Ramírez, Wei-Hsiang Liao +3
Music mastering style transfer aims to model and apply the mastering characteristics of a reference track to a target track, simulating the professional mastering process. However,…
Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution
Yonghyun Park, Chieh-Hsin Lai, Satoshi Hayakawa +7
While diffusion models excel at image generation, their growing adoption raises critical concerns about copyright issues and model transparency. Existing attribution methods identi…
Large-Scale Training Data Attribution for Music Generative Models via Unlearning
Woosung Choi, Junghyun Koo, Kin Wai Cheuk +7
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specifi…
Can Large Language Models Predict Audio Effects Parameters from Natural Language?
Seungheon Doh, Junghyun Koo, Marco A. Martínez-Ramírez +3
In music production, manipulating audio effects (Fx) parameters through natural language has the potential to reduce technical barriers for non-experts. We present LLM2Fx, a framew…
Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior
Chin-Yun Yu, Marco A. Martínez-Ramírez, Junghyun Koo +3
Style Transfer with Inference-Time Optimisation (ST-ITO) is a recent approach for transferring the applied effects of a reference audio to an audio track. It optimises the effect p…
DiffVox: A Differentiable Model for Capturing and Analysing Vocal Effects Distributions
Chin-Yun Yu, Marco A. Martínez-Ramírez, Junghyun Koo +4
This study introduces a novel and interpretable model, DiffVox, for matching vocal effects in music production. DiffVox, short for ``Differentiable Vocal Fx", integrates parametric…
TokenSynth: A Token-based Neural Synthesizer for Instrument Cloning and Text-to-Instrument
Kyungsu Kim, Junghyun Koo, Sungho Lee +2
Recent advancements in neural audio codecs have enabled the use of tokenized audio representations in various audio generation tasks, such as text-to-speech, text-to-audio, and tex…