musicassessr is an R package for facilitating the deployment of (particularly, musical) stimuli in psychological tests as well as recording and scoring data. It provides convenience functions to deploy stimuli via psychTestR, advanced psychTestR page types to collect new types of data, and utilities to process and score this data, among other things.

The musicassessr ecosystem

musicassessr is part of a network of packages. See also:

pyin: transcribe monophonic audio in the R environment using the pYIN algorithm
itembankr: produce musicassessr-compatible item banks with useful melodic features
Berkowitz: a pre-made itembankr item bank of the Baker (2021) / Berkowitz (2017) sight-singing corpus.
WJD: a pre-made itembankr item bank of the Weimar Jazz Database.

Musical ability tests

musicassessr currently facilitates the following music ability tests:

SAA (Singing Ability Assessment; Silas, Müllensiefen, & Kopiez, 2023)
PBET (Playing By Ear Test)
PDT (Pitch Discrimination Test; our re-implementation of Soranzo & Grassi, 2014)

Cheat Sheet

Analysis pipeline

Research and Documentation

You can find several articles and tutorials here, which include summarised results of research utilising this software (see: white papers). For in-depth reading, follow the results to the actual publications.

Setup

If you are running musicassessr on Linux, you will need to install SoX manually.
You will also need to install NodeJS.

References

Baker, D. (2021). MeloSol Corpus. Empirical Musicology Review, 16, 106–113. https://doi.org/10.18061/emr.v16i1.7645

Beaty, R. E., Frieler, K., Norgaard, M., Merseal, H. M., MacDonald, M. C., & Weiss, D. J. (2021). Expert musical improvisations contain sequencing biases seen in language production. Journal of Experimental Psychology. https://doi.org/10.1037/xge0001107

Berkowitz, S., Fontrier, G., Goldstein, P., & Smaldone, E. (2017). A new approach to sight singing (Sixth edition). W. W. Norton & Company.

Cannam, C., Jewell, M. O., Rhodes, C., Sandler, M., & d’Inverno, M. (2010). Linked Data And You: Bringing music research software into the Semantic Web. Journal of New Music Research, 39(4), 313–325.

Crayencour, H.-C., Velichkina, O., Frieler, K., Höger, F., Pfleiderer, M., Henry, L., Solis, G., Wolff, D., Weyde, T., Peeters, G., Basaran, D., Smith, J., & Proutskova, P. (2021). The DTL1000 Jazz Solo Dataset (in prep.). Journal on Computing and Cultural Heritage

Harrison, P. M. C. (2020). psychTestR: An R package for designing and conducting behavioural psychological experiments. Journal of Open Source Software, 5(49), 2088. https://doi.org/10.21105/joss.02088

Mauch, M., & Dixon, S. (2014). PYIN: a fundamental frequency estimator using probabilistic threshold distributions. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014).

Müllensiefen, D., & Frieler, K. (2007). Modelling experts’ notions of melodic similarity. Musicae Scientiae, 11(1_suppl), 183–210. https://doi.org/10.1177/102986490701100108

Silas, S., & Frieler, K. (2023). The musicassessr ecosystem: Record, measure, score and present feedback about musical production behaviour in real-time, supported by psychometric models. Deutsche Gesellschaft für Musikpsychologie, Hanover.

Silas, S., Müllensiefen, D., & Kopiez, R. (2023). Singing Ability Assessment: Development and validation of a singing test based on item response theory and a general open-source software environment for singing data. Behaviour Research Methods.

Silas, S., Müllensiefen, D., & Kopiez, R. (2023). Utilising a new generation of musical production tests to understand musical learning: Singing ability assessment, melodic recall and playing by ear. Deutsche Gesellschaft für Musikpsychologie, Hanover.

Silas, S., Kopiez, R., & Müllensiefen, D. (2021). What makes playing by ear difficult? SEMPRE conference.

Soranzo, A., & Grassi, M. (2014). Psychoacoustics: A comprehensive Matlab toolbox for auditory testing. Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.00712