MUS4089 - Research Internship at the Department of Musicology

Research Internships Autumn 2022

Difficulty and effort in piano playing

The student will carry out a pilot study on physical and mental effort in piano playing. The aim of the pilot study is to explore how pianists respond to music that presents different kinds of challenges (such as playing fast, playing loud chords, or playing complex rhythms). A novice pianist and a skilled pianist will participate in the study. Data collection will include electromyography (EMG, which measures physical effort through muscle activation), pupillometry (which measures mental effort through pupil size), and audio and MIDI recording. Depending on their interest, the student can choose whether to focus on EMG or pupillometry.

  • Relevant Experience with signal processing and piano playing would be relevant, but not strictly necessary.
  • Supervisor: Laura Bishop

Mapping Creative Computing Competences at UiO

The student will participate in surveying and mapping creative computing competences, practices and expertise across UiO faculties and departments. These may refer to teaching and/or research activities. This study involves the design of questionnaire, sampling of respondents, analysis of data and eventual follow up activities. The aim is to understand how many researchers and teachers at UiO use/apply creative computing approaches in their work (even when referring to these as something else than creative computing).

  • Relevant experience: generic programming competences and survey-based quantitative methods.
  • Supervisors: Stefano Fasciani, Stephen Gardener
  • Links: C2HO Network

Scholarly Impact and Citation Analysis NIME Proceedings

The student will contribute to further extend a tool we developed to study the collection of papers published since 2000 at the International Conference on New Interfaces for Musical Expression (NIME). The aim is to design and add functionalities that mine scholarly databases to perform a detailed analysis of cited papers as well as papers that cite NIME literature. Furthermore, the student can contribute to explore approaches to identify papers on musical interfaces that have been presentred at other onferences or published in other journals.

  • Relevant experience: python programming (pandas, JSON, data/graph visualization), academic referencing.
  • Researcher/Supervisor: Stefano Fasciani
  • Links: NIME2021 paper
  • Links: NIME2020 paper

Fully-Conditioned Deep Learning Modeling of Analog Audio Compressor

The student will explore and experiment modeling an analog audio compressor using deep learning techniques. This internship builds on top of an existing work in which the compressor was modeled with limited control on variable parameters (ratio and threshold only, no attack and release time) and with some undesired artifacts in the output signal. The aim is to improve the overall performances and conditioning capabilities of the current model, exploring extended architectures and custom-made cost functions. A database to carry out this work is already available, but further data can be collected (manually or automatically) if needed.

  • Relevant experience: machine learning (preferably Tensorflow in Python for raw audio) and audio analysis.
  • Researcher/Supervisor: Stefano Fasciani, Riccardo Simionato
  • Links: DAFX22 paper

Music for sleep and recovery

The student will participate in the pilot for a new applied research project carried out by ACX Music in collaboration with Olympiatoppen, The Norwegian School of Sports Sciences, and RITMO. The aim of the pilot study is to validate a recovery music program as an intervention that can potentially increase the quality of sleep in athletes, and as a result increase their level of performance. A small group of cyclists will be recruited for the two-week pilot study. The recruited cylists will listen to a curated recovery music program for 30 minutes before bedtime. Data collection will include heart rate measurements, questionnaires and a short interview.

  • Relevant experience: music psychology
  • Researcher/Supervisor: Alexander Refsum Jensenius, Agata Zelechowska (ACX Music)
  • Links: https://www.acxmusic.com/

Robot drummers

The student will explore robotic drumming using new robotic arms being developed at RITMO. Currently, two arms with different designs are available, and there is the possibility of having more. The robots allow for rapid control of the drumstick. The challenge is that the moving drum membrane makes it necessary to continuously alter the amplitude of the strokes to play even beats. It becomes particularly interesting when scaling up with multiple arms playing on one drum or multiple arms playing on separate drums. The aim of the internship will be to explore various drumming strategies and create a setup that can be used for a promo video for the 17 May celebrations next year.

Sound action analysis

The student will analyze a database of audio-video recordings of short "sound actions" that is currently being collected. A sound action is an independent perceptual unit, usually 1-5 seconds long. The project will first categorize the sound actions using the sound object taxonomy of Pierre Schaeffer (impulsive, sustained, and iterative sound types). Then the aim is to carry out various types of signal-based analyses of both audio and video using the Musical Gestures Toolbox for Python. The type of analyses performed can be tweaked to the interest of the student: more qualitatve (taxonomy-based) or more quantitative (statistical or machine learning-based).

AMBIENT

The student will investigate and pilot a setup for longterm audio and video recording in in-door environments. This will be done within the context of the new AMBIENT project starting at RITMO in September 2022. The aim of the project is to explore methods for longterm (day-long) analysis of auditory and visual rhythms in everyday environments. This requires testing and selecting cameras and microphones that can capture all of an environment, most likely a combination of ambisonics microphones and 360-degree cameras. Next, it will be necessary to figure out how they should be interfaced and analyzed to respect GDPR. It will be particularly relevant to explore the feasibility of performing realtime (or almost-realtime) audiovisual analysis.

Research internship in the PLATFORM project

We invite a research intern to contribute to the PLATFORM project, which is concerned how contemporary production technology is developed and what their significance are for music making. The research intern will be involved in carrying out an “interface study” that traces and systematizes the features and functionalities of a selection of digital audio workstations (DAWs), such as Ableton Live, Avid ProTools, Apple Logic Pro. Of particular interest is the emergence of features that uses the opportunities offered by the internet, such as online collaboration and connections to other technologies and services. In addition to looking at the DAWs themselves, the it is relevant for the study to investigate what functionalities and forms of use that are highlighted in the online marketing and promotion of the software. The study should result in a systematic comparison of the different DAWs. This “interface study” is part of Work Package 1 of the PLATFORM project (its project description will be sent to interested applicants upon request).

The research intern will be included in the PLATFORM team and will work closely with postdoctoral research fellow Emil Kraugerud, who is the primary researcher if Work Package 1, and the principal investigator of the project, Yngvar Kjus. Emil Kragerud will be the main responsible for the internship and the following up of the intern student. The research intern is expected to be actively involved in developing the research design, empirical methods and preliminary analysis of their study, in dialogue and collaboration with the project team.

Publisert 7. juli 2022 13:11 - Sist endret 27. feb. 2023 13:06
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