This repository contains materials for an 3 part lecture on the topic of computational models of music perception and cognition. The lectures are broken down into the following sections that can be put together given the level of the students:
The level of the course is aimed at upper level undergraduate and graduate students.
By the end of the lecture, the students should be able to answer:
Email David John Baker for access to deck, audio, and score files if want to edit, copy or change.
The opening section is either meant to be a slow introduction to computational models for undergraduate students who have not been exposed to this type of research before or a quicker introduction for graduate students who have some degree of familarity with this.
The lesson assumes at least some of the students in the class have either music literacy (not all) and a general interest in music perception and cognition.
The main purpose of the Computational Model slides is to introduce idea that
After a brief discussion of making sure everyone is on shared language, we move to thinking about why we might want to make models.
The point to make sure is clearly understood is that models allow for ideas to stand independelty, formalized, and ready to be critiqued by others.
Before going deeper, an example is provided where a model might predict easy or hard melodies. The purpose of this is to make clear that a model follows input output format. This leads to a discussion of ground truth and what is meant by that in this context.
After giving this difficulty of melody example, we then address several new terms
A check for understanding/synthesis of materials is done with a Think, Pair, Share with Wiggins 2007 quote on the deck.
If time permits, a plenary activity asking students to use all terms to discuss a possible research question in music perception allows for a final check for understanding before the break.
The group activity is introduced second to break more heavy, technical discussion.
Students are tasked with creating their own key finding algorithm for melodies. A brief primer is given of what is meant by key in this context (Name + Mode). Text here was written assuming students have near mimimal understanding of music notation. Audio examples are provided for each excerpt.
The task is explained with an example of how this might work.
ALERT! I have not tried this in practice yet and do not know if the instructions are clear enough or the task is too complicated
Structure the time to be
Several plenary review questions are given to capture some higher level points that will hopefully emerge from the task.
The survey and review of literature attempts to give a high level, case study inspired understanding of this area of research. The first several slides review the history of this type of research.
Then three case studies are presented looking at
These three topic areas were chosen due to my familiarity with the programs of research.
BOLDED papers are explicitly referenced in the lecture slides.
A final plenary/learning goals check is at the end of the lesson.
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Deutsch, D., & Feroe, J. (1981). The internal representation of pitch sequences in tonal music. Psychological Review, 88(6), 503–522. https://doi.org/10.1037/0033-295X.88.6.503
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Guest, O., & Martin, A. E. (2021). How Computational Modeling Can Force Theory Building in Psychological Science. 14.
Harrison, P. M. C., & Pearce, M. T. (2020). Simultaneous consonance in music perception and composition. Psychological Review, 127(2), 216–244. https://doi.org/10.1037/rev0000169
Honing, H. (2006). Computational Modeling of Music Cognition: A Case Study on Model Selection. Music Perception, 23(5), 365–376. https://doi.org/10.1525/mp.2006.23.5.365
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Large, E. W., Herrera, J. A., & Velasco, M. J. (2015). Neural Networks for Beat Perception in Musical Rhythm. Frontiers in Systems Neuroscience, 9. https://doi.org/10.3389/fnsys.2015.00159
Margulis, E. H. (2005). A Model of Melodic Expectation. Music Perception, 22(4), 663–714. https://doi.org/10.1525/mp.2005.22.4.663
Mullensiefen, D., & Wiggins, G. (n.d.). Sloboda and Parker’s recall paradigm for melodic memory: A new, computational perspective. 26.
Pearce, M. T. (2018). Statistical learning and probabilistic prediction in music cognition: Mechanisms of stylistic enculturation: Enculturation: statistical learning and prediction. Annals of the New York Academy of Sciences, 1423(1), 378–395. https://doi.org/10.1111/nyas.13654
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Sadakata, M., Desain, P., & Honing, H. (2006). The Bayesian Way to Relate Rhythm Perception and Production. Music Perception, 23(3), 269–288. https://doi.org/10.1525/mp.2006.23.3.269
Temperley, D. (2013). Computational Models of Music Cognition. In The Psychology of Music (pp. 327–368). Elsevier. https://doi.org/10.1016/B978-0-12-381460-9.00021-3
van der Steen, M. C. (Marieke), & Keller, P. E. (2013). The ADaptation and Anticipation Model (ADAM) of sensorimotor synchronization. Frontiers in Human Neuroscience, 7. https://doi.org/10.3389/fnhum.2013.00253
van der Weij, B., Pearce, M. T., & Honing, H. (2017). A Probabilistic Model of Meter Perception: Simulating Enculturation. Frontiers in Psychology, 8, 824. https://doi.org/10.3389/fpsyg.2017.00824
Vuust, P., & Witek, M. A. G. (2014). Rhythmic complexity and predictive coding: A novel approach to modeling rhythm and meter perception in music. Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.01111
Wiggins, G. A. (2007). Models of musical similarity. Musicae Scientiae, 11(1_suppl), 315–338. https://doi.org/10.1177/102986490701100112
Wiggins, G. A. (2010). Cue Abstraction, Paradigmatic Analysis and Information Dynamics: Towards Music Analysis by Cognitive Model. Musicae Scientiae, 14(2_suppl), 307–331. https://doi.org/10.1177/10298649100140S217