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Computational & Cognitive Musicology

Computational & Cognitive Musicology

The Computational and Cognitive Musicology group aims to answer questions about musical structure and organization, and how people respond to those structures, using scientific methodology. We look for new ways to build, organize, distribute, and analyze musical data, while aiming to build user-friendly technologies to assist scholars, educators, and music lovers.

 
CCML Lab

Computational and Cognitive Musicology Projects

An analysis of an Aretha Franklin song.

Music Perception and Cognition

We conduct behavioral experiments to try to answer complex problems in the field of music perception and cognition.
on-site link
A color-coded distribution of chord tones and non-chord tones.

Theory vs. Practice

We seek to provide empirical support (or refutation) for claims about the structural organization of music.
on-site link
A black and white photo of a microphone

Symbolic Corpora Building

We seek to address a severe lack of symbolic musical data and provide tools for its analysis.
on-site link
An image of musical notation with a line graf below it.

Computational Musicology Research

We aim to understand or extract higher-level features from symbolic musical data.
on-site link

2023 Publications

Arthur, C., Lehman, F., & McNamara, J. (In Press). Presenting the SWTC: A Symbolic Corpus of Themes from John Williams’ Star Wars Episodes I-IX. Empirical Musicology Review

Clark, B., & Arthur, C. (In Press). Is melody “dead”?: A large scale analysis of pop music melodies from 1960 through 2019. Empirical Musicology Review.

Condit-Schultz, N., & Clark, B. (In press). Have we sold our souls to the drum machine? A historical analysis of tempo stability in Western music recordings. Musicae Scientiae

Alben, N. & Arthur C. (2023). Pupil Dilation as a Function of Pitch Discrimination Difficulty: A Replication of Kahneman and Beatty, 1967. Attention, Perception & Psychophysics. DOI: https://doi.org/10.3758/s13414-023-02765-7

Arthur, C. & Condit-Schultz, N. (2023). The coordinated corpus of popular musics (CoCoPops): A meta-corpus of melodic and harmonic transcriptions. In Proceedings of the International Society of Music Information Retrieval (ISMIR) conference. Milan, Italy.

Arthur, C., Evans, M., McNamara, J., & Davidenko, N. (2023). Looping in your head: A Corpus of Sung Earworm Fragments. In Proceedings of the Biannual conference for the International Conference for Music Perception and Cognition (ICMPC). Tokyo, Japan

Arthur, C. (2023). Why do songs get “stuck in our heads”? Towards a theory for explaining earworms. Music & Science, 6, 1-15. DOI: https://doi.org/10.1177/2059204323116458

Jain, R. & Arthur, C. (2023). An Algorithmic Approach to Automated Symbolic Transcription of Hindustani Vocals. In Proceedings of the 10th International Digital Libraries for Musicology conference (DLfM 2023). Milan, Italy.

McNamara, J. & Arthur, C. (2023). Plugging In: Understanding Player Perceptions of Immersion and Flow in Video Games. In Proceedings of the Biannual conference for the International Conference for Music Perception and Cognition (ICMPC). Tokyo, Japan 

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