MUS4089 - Research Internship at the Department of Musicology

Research Internships Autumn 2023

Here you find the internships available for the autumn 2023 semester. Please contact the researcher/supervisor if you are interested.

Rhythmic sound vibration system for cells

What if you could use the “power of sound” to study human cells and unlock biological secrets? In this project, the student will assist with developing an embedded sound vibration system (e.g., using a speaker-based vibration generator) that will be used to study cells in a biology lab situated in Domus Medica. The system will be controlled by audio signal processing with a monitoring and feedback function. The main aim is to generate a user control interface where audio signals can be manipulated at a complex level using just a few simple parameters such as amplitude, envelope, frequency, waveform, etc. And the fun part, for those who like to tinker, will be developing the whole system to be portable and automated and possibly remotely controllable! Such practical aspects of the system will make it easy to use in any laboratory setting.

This project builds on a unique interdisciplinary collaboration between two Centres of Excellence (RITMO and HTH) and opens up a new world of research possibilities. In addition to the development of embedded system(s), the student will have the opportunity to study how cells respond over time to sound vibrations and explore how different (musical) sound parameters affect cell behavior and function.

The student will be part of the interdisciplinary team and work alongside experienced researchers, gaining valuable hands-on experience and contributing to research in the field of life sciences.

  • Necessary experience: digital signal processing (e.g., PureData, Max/MSP, or python), electronics (e.g., bela)
  • Researcher/supervisor: Dongho Kwak

Expressive and communicative gestures in orchestra conducting

This project explores the function and performance of different body gestures that are used in orchestral conducting. The aim is to show how different types of gestures, such as those used for timekeeping or cueing entrances, are shaped by the expressive quality of the music. The student will contribute to developing a taxonomy of gestures and describing them in terms of motion features. This will be done using a dataset of audio, video, and full-body motion capture suit recordings that was collected with a professional conductor during a series of live orchestral concerts. The student’s tasks will include visually studying the recordings and identifying recurring types of gestures. This process will be carried out independently by several people, who will then come together and agree on a structure for the taxonomy. The student will then extract some examples of each gesture type and compare them in terms of their motion features. For this, either video analysis or analysis of motion capture data is possible, depending on the student’s interests and expertise. The student will be encouraged to develop their own research questions to integrate into the study.

The effects of extramusical information on musical experiences

The student will help to develop the experimental materials and collect data for an online experiment investigating the effects of extramusical information on musical experiences. In the experiment, participants will receive either morally compromising, neutral, or morally favourable information about unknown artists, and subsequently listen to their music. Unfamiliar pieces of music will be randomly paired with the three types of extramusical information. After each piece, participants will be asked to report their liking, felt emotions and felt connection to the artist using a set of rating scales. The experiment is part of the Entrainment, social bonding & pleasure project at RITMO.

  • The student will be able to help with the following tasks:
    • Selecting music examples to be used in the experiment
    • Generating the three types of extramusical information
    • Setting up the experiment on an online platform (no programming skills required)
    • Data processing/analysis (optional)
  • Supervisor: Jonna Katariina Vuoskoski

Standstill installation at Popsenteret

The student will develop an installation at Popsenteret for detecting people's micromotion when standing still listening to music. The aim is to develop an exciting installation with a competitive element inspired by the Norwegian Championships of Standstill. This can be done by tracking people's head motion using an inertial measurement sensor attached to a pair of headphones and/or through video analysis. The student will work with researchers in the AMBIENT project and take part in project meetings.

Audience movement during orchestral concerts

The student will study the relationship between measured audience hand motions and the events of an orchestra concert, comparing different audiences reactions to the same program of musical works. Previous work has found dynamic relationships between audience movement and the nuances of music performance while different audiences are expected to show distinct patterns of freedom and restraint. This internship will use existing video analysis code libraries alongside music audio analysis and score analysis to explore these novel infrared camera measurements of audience behaviour and test hypotheses about what moves and stills a crowd. Within the scope of the internship, there is room for the student to define their own primary research goals. The aim of this internship is to tune research tools and test hypotheses, building on existing theory and developing new strategies for extracting and interpreting group spectator behaviour. 

  • Relevant experience: The Mocap MOOC, orchestral or large ensemble score reading skills, audio signal processing, and some programing experience (python)
  • Researcher/Supervision: Finn Upham (primary), Alexander Refsum Jensenius (secondary)

Research internship in the Engagement and Absorption project: crossmodal perception of groove in Beyoncé

The student will work as a research intern for a subproject on Beyoncé within the Engagement and Absorption project at RITMO. The aim of the intern’s work is to enable further research into the crossmodal perception of groove in Beyoncé’s performances. The project investigates how our understanding of the potential empowerment of Beyoncé’s performances can benefit from moving beyond strictly audio-visual analysis, and in addition explore how the aesthetic offered provides specific invitation to enactive, embodied engagement. At the heart of the experience is not just how spectators are fed with visual and audible input, but how - crossmodally - they are made to feel.

  • To explore and identify recent approaches to researching Beyoncé.
  • To create a Zotero library with an archive of recent Beyoncé research organised according to these recent approaches.
  • To create a literature review of some of the most relevant sources dealing with a) enactive, embodied, audio-visual engagement with popular music, and b) Beyoncé, embodiment, and groove.

For the term paper, the intern will highlight key issues and new research questions within the field. As part of the report, the student will qualify the validity of the research questions and propose means (either theoretical or practical) by which the research can be carried out.

  • Relevant experience: audio-visual analysis of music videos; analysis of groove-based music; interest in Beyoncé; interest in crossmodal perception; interest in embodied enactivist approaches to cognition.
  • Researcher/supervisor: Nanette Nielsen
  • Links: www.uio.no/ritmo/english/projects/engagement-absorption/

Research Internship in the AMBIENT project: The Ethnographic Study of Audio-Visual Background Rhythms in a Dance Experiment

The intern will be involved in a hybrid dance experiment, which will be conducted to understand the impact of space on dancers’ bodily interaction. In the experiment, dancers will perform together in different settings: together in a physical space and in hybrid settings. The intern will assist with data collection in the experiments and conduct interviews with the dancers.

The intern will also carry out short ethnographic fieldwork on their own, collecting audio-visual data from an indoor environment, including interviews and observations. The process of data analyses, interpretation and classification of audio-visual rhythms will follow the fieldwork period. The student will gain experience in ethnographical research methodologies, and they will work closely with other team members in the AMBIENT project. The AMBIENT project deals with audio-visual background rhythms of indoor environments and tries to understand how people interact with such rhythms. One of the essential aspects of the research is to combine quantitative and qualitative data to understand how people subjectively experience and bodily entrain with those rhythms.

During the internship, what is expected from the student:

  • to be part of the data collection process of the Hybrid Dance Experiment
  • to conduct their sub-ethnographic research
  • to participate in AMBIENT project meetings
  • Relevant experience: an interest in ethnographic methodologies

For more information about the internship, you can get in touch:  bilgesg@uio.no

Conditioned and Low-latency Deep Learning Modeling of Audio Effects

The student will explore and experiment modeling different audio effect using deep learning techniques that works with raw audio input/output and that accept also conditioning parameters. This internship builds on top of existing works in which an analog audio compressor was modeled with good accuracy, a full set of conditioning parameters, and the a relative input-output low-latency. The aim of the internship is to experiment with the similar architectures on a heterogeneous group of audio effects, to characterize performances and limitations. A collection of databases to carry out this work is already available, but the candidate can further collect data (using an automated system) to expand the investigation to other audio effects.

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

Research internship in the PLATFORM project: The use of DAWs in music production for audiovisual media

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 a study of how digital audio workstations (DAWs), such as Pro Tools and Reaper, is used to produce music (and other sounds) for audiovisual media, such as film and computer games. The aim is to understand to what extent the development of DAWs has influenced the making of music for audiovisual media. The study will take the perspective of musicians and examine how they approach, perceive and carry out the task of creating music suited for different media projects. The primary method will be interviews with musicians, which the research intern is expected participate in organizing, carrying out, transcribing and analysing. This study of how DAWs are used in music production for audiovisual media is part of Work Package 6 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 Professor Yngvar Kjus, who is the principal investigator of the project. Kjus 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.

Being in Concert: Audience and Performer Motion, Emotion, and Social Bonding at a Pop Concert

The student will help with data processing and analysis of motion data from motion capture and accelerometers. How does audience participation affect engagement and connectedness at a concert? A pop band performed to a live and livestreaming audience and encouraged the audience to clap and sing along to 2 songs in the set. We measured emotions with surveys and motion using Motion Capture and accelerometers. So far, we found that the live audience reported more connectedness to the other audience members than the livestreaming audience but both audiences reported similar levels of connectedness to the performers. Now we are trying to find out how the way that they moved influenced the way that they felt. The term paper would involve description 

 

Publisert 9. mars 2023 14:04 - Sist endret 11. apr. 2024 16:18
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