6 papers
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…
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…
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…
A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?
Yigitcan Özer, Woosung Choi, Joan Serrà +3
We introduce the Robust Audio Watermarking Benchmark (RAW-Bench), a benchmark for evaluating deep learning-based audio watermarking methods with standardized and systematic compari…
SteerMusic: Enhanced Musical Consistency for Zero-shot Text-guided and Personalized Music Editing
Xinlei Niu, Kin Wai Cheuk, Jing Zhang +8
Music editing is an important step in music production, which has broad applications, including game development and film production. Most existing zero-shot text-guided editing me…
Music Foundation Model as Generic Booster for Music Downstream Tasks
WeiHsiang Liao, Yuhta Takida, Yukara Ikemiya +13
We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation…