Data driven and discriminative projections for large-scale cover song identification

The predominant approach to computing document similarity in web scale applications proceeds by encoding task-specific invariance in a vectorized representation, such that the relationship between items can be computed efficiently by a simple scoring function, e.g. Euclidean distance. Here, we improve upon previous work in large-scale cover song identification by using data-driven projections at different time-scales to capture local features and embed summary vectors into a semantically organized space. We achieve this by projecting 2D-Fourier Magnitude Coefficients (2D-FMCs) of beat-chroma patches into a sparse, high dimensional representation which, due to the shift invariance properties of the Fourier Transform, is similar in principle to convolutional sparse coding. After aggregating these local beat-chroma projections, we apply supervised dimensionality reduction to recover an embedding where distance is useful for cover song retrieval. Evaluating on the Million Song Dataset, we find our method outperforms the current state of the art overall, but significantly so for top-k metrics, which indicate improved usability.