63 citations · 117 across the 16 of their papers we have counts for
17 papers
FLOMSS: Generative Music Source Separation by Flow Matching
Shuyang Cui, Zhi Zhong, Keisuke Toyama +10
Music source separation (MSS) has been conventionally addressed by discriminative methods. Recently, flow matching has emerged as a strong generative paradigm for modeling complex…
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…
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,…
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…
High-Resolution Speech Restoration with Latent Diffusion Model
Tushar Dhyani, Florian Lux, Michele Mancusi +3
Traditional speech enhancement methods often oversimplify the task of restoration by focusing on a single type of distortion. Generative models that handle multiple distortions fre…
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…
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…
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Cinematic Demixing Track
Stefan Uhlich, Giorgio Fabbro, Masato Hirano +14
This paper summarizes the cinematic demixing (CDX) track of the Sound Demixing Challenge 2023 (SDX'23). We provide a comprehensive summary of the challenge setup, detailing the str…
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai +24
This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music so…
Automatic music mixing with deep learning and out-of-domain data
Marco A. Martínez-Ramírez, Wei-Hsiang Liao, Giorgio Fabbro +3
Music mixing traditionally involves recording instruments in the form of clean, individual tracks and blending them into a final mixture using audio effects and expert knowledge (e…
Distortion Audio Effects: Learning How to Recover the Clean Signal
Johannes Imort, Giorgio Fabbro, Marco A. Martínez Ramírez +3
Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing syste…
Music Source Separation with Deep Equilibrium Models
Yuichiro Koyama, Naoki Murata, Stefan Uhlich +3
While deep neural network-based music source separation (MSS) is very effective and achieves high performance, its model size is often a problem for practical deployment. Deep impl…
Music Demixing Challenge 2021
Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich +6
Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIR…
Training Speech Enhancement Systems with Noisy Speech Datasets
Koichi Saito, Stefan Uhlich, Giorgio Fabbro +1
Recently, deep neural network (DNN)-based speech enhancement (SE) systems have been used with great success. During training, such systems require clean speech data - ideally, in l…
Speech Synthesis and Control Using Differentiable DSP
Giorgio Fabbro, Vladimir Golkov, Thomas Kemp +1
Modern text-to-speech systems are able to produce natural and high-quality speech, but speech contains factors of variation (e.g. pitch, rhythm, loudness, timbre)\ that text alone…