  This paper investigates a cross-modal retrieval problem in which a user would
like to retrieve a passage of music from a MIDI file by taking a cell phone
picture of a physical page of sheet music. While audio-sheet music retrieval
has been explored by a number of works, this scenario is novel in that the
query is a cell phone picture rather than a digital scan. To solve this
problem, we introduce a mid-level feature representation called a bootleg score
which explicitly encodes the rules of Western musical notation. We convert both
the MIDI and the sheet music into bootleg scores using deterministic rules of
music and classical computer vision techniques for detecting simple geometric
shapes. Once the MIDI and cell phone image have been converted into bootleg
scores, we estimate the alignment using dynamic programming. The most notable
characteristic of our system is that it does test-time adaptation and has no
trainable weights at all -- only a set of about 30 hyperparameters. On a
dataset containing 1000 cell phone pictures taken of 100 scores of classical
piano music, our system achieves an F measure score of .869 and outperforms
baseline systems based on commercial optical music recognition software.
