Prem Seetharaman, Fatemeh Pishdadian, Bryan Pardo, Ethan Manilow (implementation author) Northwestern University [email protected]
- is_blind: yes
- additional_training_data: no
- Code: https://github.com/interactiveaudiolab/nussl/blob/master/nussl/separation/ft2d.py
- Demos: https://interactiveaudiolab.github.io/demos/2dft
This is the nussl implementation of foreground/background separation using the 2D Fourier Transform.
Abstract from original paper:
Audio source separation is the act of isolating sound sources
in an audio scene. One application of source separation is
singing voice extraction. In this work, we present a novel
approach for music/voice separation that uses the 2D Fourier
Transform (2DFT). Our approach leverages how periodic
patterns manifest in the 2D Fourier Transform and is con-
nected to research in biological auditory systems as well as
image processing. We find that our system is very simple to
describe and implement and competitive with existing unsu-
pervised source separation approaches that leverage similar
assumptions.
- Prem Seetharaman, Fatemeh Pishdadian, and Bryan Pardo. Music/voice separation using the 2d fourier transform. In Applications of Signal Processing to Au- dio and Acoustics (WASPAA), 2017 IEEE Workshop on, pages 36–40. IEEE, 2017.