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Cover song recognition, also known as version identification, can only be solved by exposing the underlying tonal content of music. Apart from obvious applications in copyright enforcement, techniques for cover identification can also be used to find patterns and structure in music datasets too large for any musicologist to listen to even once. Much progress has been made on cover song recognition, but work to date has been reported on datasets of at most a few thousand songs, using algorithms that simply do not scale beyond the capacity of a small portable music player. In this paper, we consider the problem of finding covers in a database of a million songs, considering only algorithms that can deal with such data. Using a fingerprinting-inspired model, we present the first results of cover song recognition on the Million Song Dataset. The availability of industrial-scale datasets to the research community presents a new frontier for version identification, and this work is intended to be the first step toward a practical solution.
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