  This paper addresses the problem of cross-modal musical piece identification
and retrieval: finding the appropriate recording(s) from a database given a
sheet music query, and vice versa, working directly with audio and scanned
sheet music images. The fundamental approach to this is to learn a cross-modal
embedding space with a suitable similarity structure for audio and sheet image
snippets, using a deep neural network, and identifying candidate pieces by
cross-modal near neighbour search in this space. However, this method is
oblivious of temporal aspects of music. In this paper, we introduce two
strategies that address this shortcoming. First, we present a strategy that
aligns sequences of embeddings learned from sheet music scans and audio
snippets. A series of experiments on whole piece and fragment-level retrieval
on 24 hours worth of classical piano recordings demonstrates significant
improvement. Second, we show that the retrieval can be further improved by
introducing an attention mechanism to the embedding learning model that reduces
the effects of tempo variations in music. To conclude, we assess the
scalability of our method and discuss potential measures to make it suitable
for truly large-scale applications.
