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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.

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