A Deep Learning Approach to Intelligent Drum Mixing With the Wave-U-Net

Martínez Ramírez MA, Stoller D, Moffat D. A Deep Learning Approach to Intelligent Drum Mixing With the Wave-U-Net. J Audio Eng Soc. 2021;69(3):142-151. doi:10.17743/jaes.2020.0031

@article{MartinezRamirez2021_21023,
  author = {Martínez Ramírez, Marco A. and Stoller, Daniel and Moffat, David},
  title = {{A Deep Learning Approach to Intelligent Drum Mixing With the Wave-U-Net}},
  journal = {Journal of the Audio Engineering Society},
  volume = {69},
  number = {3},
  pages = {142--151},
  year = {2021},
  month = mar,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2020.0031},
  url = {https://doi.org/10.17743/jaes.2020.0031}
}

TY  - JOUR
TI  - A Deep Learning Approach to Intelligent Drum Mixing With the Wave-U-Net
AU  - Martínez Ramírez, Marco A.
AU  - Stoller, Daniel
AU  - Moffat, David
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 69
IS  - 3
SP  - 142
EP  - 151
PY  - 2021
DA  - 2021/03/06
DO  - 10.17743/jaes.2020.0031
UR  - https://doi.org/10.17743/jaes.2020.0031
PB  - Audio Engineering Society
LA  - en
AB  - The development of intelligent music production tools has been of growing interest in recent years. Deep learning approaches have been shown as being a highly effective method for approximating individual audio effects. In this work, we propose an end-to-end deep neural network based on the Wave-U-Net to perform automatic mixing of drums. We follow an end-to-end approach where raw audio from the individual drum recordings is the input of the system and the waveform of the stereo mix is the output. We compare the system to existing machine learning approaches to intelligent drum mixing. Through a subjective listening test we explore the performance of these systems when processing various types of drum mixes. We report that the mixes generated by our model are virtually indistinguishable from professional human mixes while also outperforming previous intelligent mixing approaches.
ER  -