Don't Separate, Learn to Remix: End-to-End Neural Remixing with Joint Optimization

28 July 2021

Papers citing "Don't Separate, Learn to Remix: End-to-End Neural Remixing with Joint Optimization"

6 / 6 papers shown

Title
Prevailing Research Areas for Music AI in the Era of Foundation Models Megan Wei M. Modrzejewski Aswin Sivaraman Dorien Herremans MedIm 35 1 0 14 Sep 2024
General Purpose Audio Effect Removal Matthew Rice C. Steinmetz Georgy Fazekas Joshua D. Reiss 27 8 0 30 Aug 2023
InstructME: An Instruction Guided Music Edit And Remix Framework with Latent Diffusion Models Bing Han Junyu Dai Weituo Hao Xinyan He Dong Guo Jitong Chen Yuxuan Wang Y. Qian Xuchen Song DiffM 24 15 0 28 Aug 2023
Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks Darius Petermann G. Wichern Aswin Shanmugam Subramanian Zhong-Qiu Wang Jonathan Le Roux 27 10 0 14 Dec 2022
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity Ethan Manilow G. Wichern Prem Seetharaman Jonathan Le Roux 54 122 0 18 Sep 2019
Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation Daniel Stoller Sebastian Ewert S. Dixon AI4TS 104 588 0 08 Jun 2018