Piano Sheet Music Identification Using Marketplace Fingerprinting

This paper studies the problem of identifying piano sheet music based on a cell phone image of all or part of a physical page. We re-examine current best practices for large-scale sheet music retrieval through an economics perspective. In our analogy, the runtime search is like a consumer shopping in a store. The items on the shelves correspond to fingerprints, and purchasing an item corresponds to doing a fingerprint lookup in the database. From this perspective, we show that previous approaches are extremely inefficient marketplaces in which the consumer has very few choices and adopts an irrational buying strategy. The main contribution of this work is to propose a novel fingerprinting scheme called marketplace fingerprinting. This approach redesigns the system to be an efficient marketplace in which the consumer has many options and adopts a rational buying strategy that explicitly considers the cost and expected utility of each item. We also show that de-ciding which fingerprints to include in the database poses a type of minimax problem in which the store and the consumer have competing interests. On experiments using all solo piano sheet music images in IMSLP as a searchable database, we show that marketplace fingerprinting substantially outperforms previous approaches and achieves a mean reciprocal rank of 0 . 905 with sub-second average runtime.