Large-scale cover song recognition using hashed chroma landmarks

Cover song recognition, also called 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 even listen to. 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, and we only consider 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. This task has been renewed by the availability of so many tracks, and this work is intended to be the first step towards a practical solution.