Sensing Sound and Music - Sensing Sound and Music

Introduction¶

This textbook is written for the course MUS2640 Sensing Sound and Music at the University of Oslo, a foundation course for later studies in music psychology and music technology. It introduces how musical sound is produced in the world, represented technologically, and experienced in body and mind.

Course schedule¶

The course is organised for 12 teaching weeks with 2–3 hours of in-class work per week. You should plan roughly 6 hours of reading and assignments outside class each week. The course schedule below maps each week to a chapter, core ideas, and suggested activities.

Week	Chapter	Focus	In-class (theory)	In-class or home (applied)
1	Tuning in	Sensing, sound, music; interdisciplinarity; how this course is structured	Course goals, fields, disciplinarities	Define sensing, sound, music in your own words; try one app from the intro list
2	Listening	Soundscape, ways of describing sound, artistic and scientific approaches	Sound studies vs acoustics vs psychoacoustics	Two-minute closed-eyes listen; short sound journal
3	Acoustics	Waves, rooms, instruments: causes of what we hear	Cause → propagation → reception chain	Sonic Visualiser plots on a sound you choose
4	Electroacoustics	Capture, conversion, digital audio, monitors: technology chain	AD/DA, microphones, loudspeakers	Recording chain for one of your own setups; LittleBits synth-kit exploration
5	Psychoacoustics	Ear, loudness, pitch, masking, illusions: from physics to perception	Thresholds, critical bands, streaming	Demo predictions for masking / continuity illusion
6	Time and rhythm	Metre, groove, micro-timing, entrainment	Pulse vs metre; groove vocabulary	Tap-along timing capture
7	Harmony and melody	Spectral and symbolic views; expectation and structure	Scales, chords, chroma	Short harmonic/melodic annotation
8	The body	Embodiment, gestures, motion capture	Performer vs perceiver; embodied cognition	Record or describe body motion while listening or performing
9	Physiology	Autonomic responses; HR, HRV, EDA, breathing	Sympathetic vs parasympathetic	Relate one physiological idea to your own listening
10	Vision	Auditory–visual integration, eye tracking, multimedia	AV vs audio–video; selective attention	Note where you look vs what you hear
11	The brain	Regions, pathways, cognition, emotion networks; neuroscience methods	Methods trade-offs	Identify claim–evidence–method
12	Machine listening	Audio features, MIR, ML; data and AI ethics	Pipeline: audio → features → task	Run or inspect one feature plot

Alongside the twelve chapters there is a thirteenth page, Tips and tricks, which is not tied to any week. It collects the practical craft the course assumes but does not teach in class: how to write about sound, how to cite recordings or works, how to build a figure someone else can read, and how to prepare for the exam. It is meant to be consulted when you are planning a text or revising, rather than read straight through.

You can also check out the Glossary, which is a compilation of terms with descriptions.

Pedagogical strategy¶

Students in this course typically have mixed backgrounds. Most have some musical experience, ranging from classical performance on acoustic instruments to electronic studio production, but non-performing music-lovers are equally welcome.

Some students come from musicology, others from psychology, informatics, media studies, philosophy, linguistics, medicine, and more. This mix is what makes the interdisciplinary discussions in class possible, and the in-class activities are adjusted to the collective knowledge and experience of the group.

Active learning and “flipped classroom”¶

The course builds on active learning. Instead of sitting through lectures and taking notes, you work with the material: you discuss it, ask questions, try things out, and respond to each other’s work. Much of the class time is spent on group discussions, hands-on experiments, peer review, and small projects.

This goes together with a flipped classroom approach. It requires that you read, watch, and listen on your own. Then we can spend class time for discussing it and do hands-on activities.

A research-based and research-led course¶

This is a research-based course. The content builds on current research, and the teachers are all active researchers who bring in perspectives from their own projects. Much of this work follows scientific methods, but because of the subject matter we also draw on design-centred and artistic research. We will return to these differences along the way, since the epistemological question of how each tradition justifies what it claims to know is itself worth understanding.

The course is also research-led. You will take part in some ongoing research activities. This lets you see how research is done in practice, and it feeds back into ongoing projects at the Department of Musicology and RITMO. You are welcome, though not required, to join in, for example, by contributing own data or taking part in one of our ongoing studies.

Reading empirical research¶

This course aims at merging theories and methods from musicology, music psychology, and music technology (and others). These are fields with different research traditions. When you read a paper or a summary, it can help to use a simple checklist for yourself:

Claim — What is the main point of this study?
Evidence — What data or materials support it (listeners, musicians, audio examples, corpora, brain measures, motion capture, questionnaires)?
Method — How were participants or materials chosen? What was measured or analysed? What baseline or control was used?
Limits — What does this study not show (culture-specific listeners, short stimuli, lab vs concert, correlational vs causal)? What would falsify or qualify the claim?

These habits align with the course’s mix of quantitative experiments, qualitative inquiry, critical scholarship, and practice-based research. When it is your turn to write up what you have read, the sections on academic writing, writing about music, and referencing in Tips and tricks pick up where this checklist leaves off.

Open education¶

The course material follows the idea of Open Education. Everything here is freely and openly available, during the course and afterwards. Open material is easier to share, revisit, and build on, both inside the university and beyond it.

This sits close to Open Research and its emphasis on transparency, accessibility, and reproducibility. In this course we try to follow those principles by sharing:

Open Publications: Most of the required reading is openly available, either genuinely free (“free as in speech”) or through the university’s institutional agreements (“free as in beer”).
Open Data: Where we can, the datasets used in the course are shared openly, so you can analyse, visualise, and interpret them yourself.
Open Source Software: The tools, scripts, and examples are shared as open-source code that you can read, modify, and reuse.

Working this way also makes it easier for you to contribute back rather than only consume.

Embracing AI¶

In this course we actively explore artificial intelligence (AI). Many AI tools exist, but few are well understood in teaching. We will try several and judge for ourselves how well they work.

This textbook is itself an example of AI-assisted co-creation. It has been written with the support of various large language models (LLMs). However, every part has been read and corrected by a human. During the course we will look at where AI writing can be trusted and where it breaks down.

You are encouraged to use AI for learning, but to use it wisely. It can explain a concept or give you quick feedback, but it is there to support your thinking, not to replace it. The exam is done without any tools, so in the end you have to be able to think and write on your own.

Tools¶

We will try out a range of tools during the semester. The aim is not to master any single one, but to see how each works and what it is suited for.

Curriculum¶

The current textbook comprises the core curriculum for this course. Interested readers can find more information in the texts listed below, as well as in the references section for each chapter.

Learn more¶

MUS2133 Music Psychology

This course covers the psychological aspects of music perception and cognition, including auditory perception, emotional responses to music, and the cognitive processes involved in listening, performing, and composing.

MUS2850 Computer Music

This course covers the meeting point of music and technology: sound synthesis, digital audio processing, and algorithmic composition, with hands-on work using tools for creating and analysing music computationally.

References¶

Margulis, E. H. (2018). The Psychology of Music: A Very Short Introduction. Oxford University Press. 10.1093/actrade/9780190640156.001.0001
Katz, M. (2022). Music and Technology: A Very Short Introduction. Oxford University Press. 10.1093/actrade/9780199946983.001.0001
Clarke, E. F. (2005). Ways of Listening: An Ecological Approach to the Perception of Musical Meaning. Oxford University Press. 10.1093/acprof:oso/9780195151947.001.0001
Jensenius, A. R. (2022). Sound Actions: Conceptualizing Musical Instruments. The MIT Press. 10.7551/mitpress/14220.001.0001
Bader, R. (Ed.). (2018). Springer Handbook of Systematic Musicology. Springer Berlin Heidelberg. 10.1007/978-3-662-55004-5
Hallam, S., Cross, I., & Thaut, M. (Eds.). (2014). The Oxford Handbook of Music Psychology, Second Edition. Oxford University Press. 10.1093/oxfordhb/9780198722946.001.0001