Piano Sheet Music Identification Using Dynamic N-gram Fingerprinting

This article introduces a method for large-scale retrieval of piano sheet music images. We study this problem in two different scenarios: camera-based sheet music identification and MIDI-sheet image retrieval. Our proposed method combines bootleg score features with a novel hashing scheme called dynamic N-gram fingerprinting. This hashing scheme ensures that every fingerprint is discriminative enough to warrant a table lookup, which improves both retrieval accuracy and runtime. On experiments using all piano sheet music images in the IMSLP database, the proposed method achieves >0.8 mean reciprocal rank with subsecond runtimes. As a practical application, we use our system to find matches between the Lakh MIDI dataset and IMSLP, which augments the IMSLP sheet music data with symbolic music information for a subset of pieces. We release our code and Lakh-IMSLP matches to facilitate future study.