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The Norwegian Career Assessment Matrix (NOR-CAM) is a toolbox for recognition and rewards in academic careers. The presentation is licensed with CC-BY 4.0., Presentation held at the IAA-CSIC Severo Ochoa SKA Open Science School, Granada, 9 May 2023

Supplemental material, sj-docx-1-mns-10.1177_20592043231168597 for Into the Hive-Mind: Shared Absorption and Cardiac Interrelations in Expert and Student String Quartets by Simon Høffding, Wenbo Yi, Eigil Lippert, Victor Gonzales Sanchez, Laura Bishop, Bruno Laeng, Anne Danielsen, Alexander Refsum Jensenius and Sebastian Wallot in Music & Science

Supplementary material for the paper "Comparing Inertial Motion Sensors for Capturing Human Micromotion" presented at the Sound and Music Computing Conference in Stockholm, June 2023.

Data from the year-long StillStanding project

Hvilke dilemmaer oppstår når forskningsdata og resultater skal deles og gjenbrukes? Og hvilke muligheter medfører mer åpenhet og økt deling av data for fag som eksempelvis musikk, visuell kunst, film, scenekunst og design?, Presentasjon på seminaret "Kunstfag og deling av forskningsdata og resultater - utfordringer og muligheter" organisert 15. november 2022 ved Universitetet i Oslo.

The slides that were used for a presentation held for the Directorate for Higher Education and Skills in Norway on 10 November 2022. The presentation presented the Norwegian Career Assessment Matrix (NOR-CAM), a flexible and holistic framework for recognition and rewards in academic research assessment.The ambition has been to develop a guide that adopts three core principles for assessment: more transparency, greater breadth, and comprehensive assessments as opposed to the one-sided use of indicators.

Slides for a presentation held at the RIOT Science Club. Abstract: Many music researchers are turning towards studying music performance and perception in real-world settings. Collecting data in a concert situation is non-trivial, and FAIRifying the data is even more challenging. In this talk, I will discuss some challenges with handling privacy and copyright matters in music research. I will also discuss some benefits of working towards more open music research.

Intelligent robots aimed for performing music and playing musical instruments have been developed in recent years. With the advancements in artificial intelligence and robotic systems, new capabilities have been explored in this field. One major aspect of musical robots that can lead to the emergence of creative results is the ability to learn skills autonomously. To make it feasible, it is important to make the robot utilize its maximum potential and mechanical capabilities to play a musical instrument. Furthermore, the robot needs to find the musical possibilities based on the physical properties of the instrument to provide satisfying results. In this work, we introduce a drum robot with certain mechanical specifications and analyze the capabilities of the robot according to the drumming sound results of the robot. The robot has two degrees of freedom, actuated by one quasi direct-drive servo motor. The gripper of the robot features a flexible joint with passive springs which adds complexity to the movements of the drumstick. In a basic experiment, we have looked at the drum roll performance by the robot while changing a few control variables such as frequency and amplitude of the motion. Both single-stroke and double-stroke drum rolls can be performed by the robot by changing the control variables. The effect of the flexible gripper on the drumming results of the robot is the main focus of this study. Additionally, we have divided the control space according to the type of drum rolls. The results of this experiment lay the groundwork for developing an intelligent algorithm for the robot to learn musical patterns by interacting with the drum.

The paper presents the Musical Gestures Toolbox (MGT) for Python, a collection of modules targeted at researchers working with video recordings. The toolbox includes video visualization techniques such as creating motion videos, motion history images, and motiongrams. These visualizations allow for studying video recordings from different temporal and spatial perspectives. The toolbox also includes basic computer vision methods, and it is designed to integrate well with audio analysis toolboxes. The MGT was initially developed to analyze music-related body motion (of musicians, dancers, and perceivers) but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, pedagogy, psychology, and medicine.