Optical Music Recognition: State of the Art and Major Challenges

Optical Music Recognition (OMR) is concerned with transcribing sheet music into a machine-readable format. The transcribed copy should allow musicians to compose, play and edit music by taking a picture of a music sheet. Complete transcription of sheet music would also enable more efficient archival. OMR facilitates examining sheet music statistically or searching for patterns of notations, thus helping use cases in digital musicology too. Recently, there has been a shift in OMR from using conventional computer vision techniques towards a deep learning approach. In this paper, we review relevant works in OMR, including fundamental methods and significant outcomes, and highlight different stages of the OMR pipeline. These stages often lack standard input and output representation and standardised evaluation. Therefore, comparing different approaches and evaluating the impact of different processing methods can become rather complex. This paper provides recommendations for future work, addressing some of the highlighted issues and represents a position in furthering this important field of research

FEATUR.UX: An approach to leveraging multitrack information for artistic music visualization

FEATUR.UX (Feature - ous) is an audio visualisation tool, currently in the process of development, which proposes to introduce a new approach to sound visualisation using pre-mixed, independent multitracks and audio feature ex- traction. Sound visualisation is usually performed using a mixed mono or stereo track of audio. Audio feature ex- traction is commonly used in the field of music information retrieval to create search and recommendation systems for large music databases rather than generating live visual- isations. Visualizing multitrack audio circumvents prob- lems related to the source separation of mixed audio sig- nals and presents an opportunity to examine interdepen- dent relationships within and between separate streams of music. This novel approach to sound visualisation aims to provide an enhanced listening experience in a use case that employs non-tonal, non-notated forms of electronic music. Findings from prior research studies focused on live per- formance and preliminary quantitative results from a user survey have provided the basis from which to develop a prototype for an iterative design study that examines the impact of using multitrack audio and audio feature extrac- tion within sound visualisation practice