Music segment similarity using 2D-Fourier Magnitude Coefficients

Music segmentation is the task of automatically identifying the different segments of a piece. In this work we present a novel approach to cluster the musical segments based on their acoustic similarity by using 2D-Fourier Magnitude Coefficients (2D-FMCs). These coefficients, computed from a chroma representation, significantly simplify the problem of clustering the different segments since they are key transposition and phase shift invariant. We explore various strategies to obtain the 2D-FMC patches that represent entire segments and apply k-means to label them. Finally, we discuss possible ways of estimating k and compare our competitive results with the current state of the art.

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