2DFT

Prem Seetharaman, Fatemeh Pishdadian, Bryan Pardo, Ethan Manilow (implementation author) Northwestern University ethanmanilow@u.northwestern.edu

Additional Info

• is_blind: yes
• additional_training_data: no

Supplementary Material

• Code: https://github.com/interactiveaudiolab/nussl/blob/master/nussl/separation/ft2d.py
• Demos: https://interactiveaudiolab.github.io/demos/2dft

Method

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.

References

• 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.