26 citations · 39 across the 13 of their papers we have counts for
5 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…
VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance
Carlos Hernandez-Olivan, Koichi Saito, Naoki Murata +4
Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation. Recent diffusion-based music restoration methods have demonstrated impress…
Timbre-Trap: A Low-Resource Framework for Instrument-Agnostic Music Transcription
Frank Cwitkowitz, Kin Wai Cheuk, Woosung Choi +4
In recent years, research on music transcription has focused mainly on architecture design and instrument-specific data acquisition. With the lack of availability of diverse datase…
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…