2 citations · 2 across the 5 of their papers we have counts for
13 papers · 1 filter
LLM2Fx-Tools: Tool Calling For Music Post-Production
Seungheon Doh, Junghyun Koo, Marco A. Martínez-Ramírez +5
This paper introduces LLM2Fx-Tools, a multimodal tool-calling framework that generates executable sequences of audio effects (Fx-chain) for music post-production. LLM2Fx-Tools uses…
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,…
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…