Cover song identification with 2D Fourier Transform sequences

Prem Seetharaman; Zafar Rafii

ICASSP 2017accepted0 citations

Cover song identification with 2D Fourier Transform sequences

Abstract

We approach cover song identification using a novel time-series representation of audio based on the 2DFT. The audio is represented as a sequence of magnitude 2D Fourier Transforms (2DFT). This representation is robust to key changes, timbral changes, and small local tempo deviations. We look at cross-similarity between these time-series, and extract a distance measure that is invariant to music structure changes. Our approach is state-of-the-art on a recent cover song dataset, and expands on previous work using the 2DFT for music representation and work on live song recognition.

BibTeX

@inproceedings{icassp2017_coversongidentif,
  title = {Cover song identification with 2D Fourier Transform sequences},
  author = {Prem Seetharaman and Zafar Rafii},
  booktitle = {ICASSP 2017},
  year = {2017}
}