MSMD is a synthetic dataset of 497 pieces of (classical) music that contains both audio and score representations of the pieces aligned at a fine-grained level (344,742 pairs of noteheads aligned to their audio/MIDI counterpart). It can be used for training and evaluating multimodal models that enable crossing from one modality to the other, such as retrieving sheet music using recordings or following a performance in the score image. Please find further information and a corresponding Python package on this Github page: https://github.com/CPJKU/msmd If you use this dataset, please cite: [1] Matthias Dorfer, Jan Hajič jr., Andreas Arzt, Harald Frostel, Gerhard Widmer. Learning Audio-Sheet Music Correspondences for Cross-Modal Retrieval and Piece Identification (PDF). Transactions of the International Society for Music Information Retrieval, issue 1, 2018.