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Chroma Feature Abstraction using Multiscale 2D-FTM and N-gram for Cover Song Search

We proposed a chroma feature sequence abstraction method that can, when compared with conventional methods, improve the speed of cover song identification methods so that it can be used in large-scale music identification services. The proposed method represents the chroma feature sequence, which is proportional to the length of music, in fixed length feature vectors using multiscale 2D-FTM (two-dimensional Fourier-transform magnitude) and chroma n-gram. It facilitates indexing and simplifies distance comparison to improve identification speed. The abstracted chroma vector maintains the global and local characteristics of the chroma feature sequence, making it more robust to the tempo change, transposition, and remake versions of a song. We experimented using public datasets and large-scale datasets collected from the YouTube website in order to verify the performance of the proposed method, and we confirmed that search speed was improved while maintaining the cover song identification performance.