Honoursmodule: The Data Science of Everyday Music Listening

Course manual 2019/2020

Course content

Working in small groups, students in this course learn how to synthesise heterogeneous sources of quantitative data to understand more about their own daily musical experiences and the musical experiences of others. The course takes a breadth-first approach so that (1) students are exposed to as many areas as possible where they can apply skills they have developed in other courses to music and (2) students with a strong interest in the subject are prepared to deepen their understanding in courses such as Computational Musicology or Cognitive Musicology. Each group designs, executes, and analyses a small research experiment on everyday music listening, incorporating questionnaires from the music cognition literature and listening histories from streaming services like Spotify and Apple Music, guided and inspired by a serious of lectures and practical sessions on research topics and tools from cognitive and computational musicology.

The first half of the course focuses on designing a good research experiment, including practical sessions about tools like the Spotify Developer API and online surveys with Qualtrics. Students are also exposed to major trends in quantitative musicological research and write a short article review.

In the second half of the course, the course content shifts its focus toward analysis and visualisation techniques. Students build up an online portfolio to document their experiments and results. After the final presentations, each student chooses a research project about which to write an extended critique, with an emphasis on potential directions for further study.

Study materials

Literature

• Weekly readings available on Canvas or from the university library.

Practical training material

• Available on Canvas.

Software

• Spotify, R/RStudio, Git/Github, and Qualtrics

Objectives

• recognise common quantitative measurements used in music research,
• compare musical audio collections quantitatively using standard features from music information retrieval,
• measure musical behaviour using verified instruments from music cognition,
• discriminate among audio collections and listening behaviours on the basis of simple statistical models,
• design survey-based studies of music listening in everyday life,
• judge the quality of quantitative musical research and provide constructive criticisms for future work.

Teaching methods

• Lecture
• Laptop seminar
• Presentation/symposium
• Self-study
• Working independently on e.g. a project or thesis
• Computer lab session/practical training

Learning activities

Attendance

Additional requirements for this course:

Absence needs to be communicated to the course coordinator.

Assessment

Inspection of assessed work

Student may discuss any course results with the instructor during office hours.

Assignments

Students will work in groups to design and execute an experiment about everyday music listening. This group project will be evaluated at two moments:

• a mid-term presentation describing the research questions and study design; and
• an online portfolio (and short presentation thereof) presenting the results.

Students will work individually to deepen their understanding of specific topics related to everyday music listening. This individual deepening of understanding will be evaluated at two moments:

• a short article review of an important piece of academic literature outside of those discussed in class; and
• a longer critique of one of the other groups' portfolios, contextualising its strengths, weaknesses, and potential for future research with reference to external academic literature.

A small portion of the final grade consists of peer reviews of other portfolios as they develop throughout the second half of the course.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Timetable

The schedule for this course is published on DataNose.

Last year's course evaluation

In order to provide students some insight how we use the feedback of student evaluations to enhance the quality of education, we decided to include the table below in all course guides.

Contact information

Coordinator

• dr. J.A. Burgoyne

Office-hour appointments are available at calendly.com/jaburgoyne