63 citations · 235 across the 27 of their papers we have counts for
28 papers
Reverse Engineering of Music Mixing Graphs with Differentiable Processors and Iterative Pruning
Sungho Lee, Marco Martínez-Ramírez, Wei-Hsiang Liao +4
Reverse engineering of music mixes aims to uncover how dry source signals are processed and combined to produce a final mix. We extend the prior works to reflect the compositional…
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…
Latent Diffusion Bridges for Unsupervised Musical Audio Timbre Transfer
Michele Mancusi, Yurii Halychanskyi, Kin Wai Cheuk +8
Music timbre transfer is a challenging task that involves modifying the timbral characteristics of an audio signal while preserving its melodic structure. In this paper, we propose…
GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch
Sungho Lee, Marco Martínez-Ramírez, Wei-Hsiang Liao +4
We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the…
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning
Bac Nguyen, Stefan Uhlich, Fabien Cardinaux +3
Handling distribution shifts from training data, known as out-of-distribution (OOD) generalization, poses a significant challenge in the field of machine learning. While a pre-trai…
Searching For Music Mixing Graphs: A Pruning Approach
Sungho Lee, Marco A. Martínez-Ramírez, Wei-Hsiang Liao +4
Music mixing is compositional -- experts combine multiple audio processors to achieve a cohesive mix from dry source tracks. We propose a method to reverse engineer this process fr…