Automated Arrangements of Multi-Part Music for Sets of Monophonic Instruments

Arranging music for a different set of instruments that it was originally written for is traditionally a tedious and time-consuming process, performed by experts with intricate knowledge of the specific instruments and involving significant experimentation. In this paper we study the problem of automating music arrangements for music pieces written for monophonic instruments or voices. We designed and implemented an algorithm that can always produce a music arrangement when feasible by transposing the music piece to a different scale, permuting the assigned parts to instruments/voices, and transposing individual parts by one or more octaves. We also published open source software written in Python that processes MusicXML files and allows musicians to experiment with music arrangements. It is our hope that our software can serve as a platform for future extensions that will include music reductions and inclusion of polyphonic instruments.

Diagnostic Captioning: A Survey

Diagnostic Captioning (DC) concerns the automatic generation of a diagnostic text from a set of medical images of a patient collected during an examination. DC can assist inexperienced physicians, reducing clinical errors. It can also help experienced physicians produce diagnostic reports faster. Following the advances of deep learning, especially in generic image captioning, DC has recently attracted more attention, leading to several systems and datasets. This article is an extensive overview of DC. It presents relevant datasets, evaluation measures, and up to date systems. It also highlights shortcomings that hinder DC's progress and proposes future directions.