Camera-Based Piano Sheet Music Identification
It enables a practical, user-facing application for musicians or students to identify sheet music quickly, though it is incremental as it builds on prior fingerprinting methods.
This paper tackles the problem of large-scale retrieval of piano sheet music images from cell phone photos, achieving a mean reciprocal rank of 0.85 and an average runtime of 0.98 seconds per query.
This paper presents a method for large-scale retrieval of piano sheet music images. Our work differs from previous studies on sheet music retrieval in two ways. First, we investigate the problem at a much larger scale than previous studies, using all solo piano sheet music images in the entire IMSLP dataset as a searchable database. Second, we use cell phone images of sheet music as our input queries, which lends itself to a practical, user-facing application. We show that a previously proposed fingerprinting method for sheet music retrieval is far too slow for a real-time application, and we diagnose its shortcomings. We propose a novel hashing scheme called dynamic n-gram fingerprinting that significantly reduces runtime while simultaneously boosting retrieval accuracy. In experiments on IMSLP data, our proposed method achieves a mean reciprocal rank of 0.85 and an average runtime of 0.98 seconds per query.