Cover song identification using 2DFT sequences
Jupyter Notebook Python
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
datasets
presentation
.gitignore
README.md

README.md

Cover song identification using 2D Fourier transform sequences

Prem Seetharaman and Zafar Rafii, Summer 2016

This notebook presents a cover identification algorithm based on the Magnitude 2DFT.

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.

This work was done at Gracenote, inc.