Dear list members,
We are delighted to announce the publication of a special collection in the journal, Music & Science, on the topic of 'Explaining music with AI: Advancing the scientific understanding of music through computation'. The collection has been guest edited by David Meredith, Anja Volk and Tom Collins.
The special collection has been published open access and is available online at
https://journals.sagepub.com/topic/collections-mns/mns-1-explaining_music_with_ai/mns
The collection includes an editorial overview and seven articles as follows:
Perception of Chord Sequences Modeled with Prediction by Partial Matching, Voice-Leading Distance, and Spectral Pitch-Class Similarity: A New Approach for Testing Individual Differences in Harmony Perception, by Matthew Eitel, Nicolas Ruth, Peter Harrison, Klaus Frieler, and Daniel Müllensiefen.
https://doi.org/10.1177/20592043241257654
The Interconnections of Music Structure, Harmony, Melody, Rhythm, and Predictivity, by Shuqi Dai, Huan Zhang, and Roger B. Dannenberg.
https://doi.org/10.1177/20592043241234758
End-to-End Bayesian Segmentation and Similarity Assessment of Performed Music Tempo and Dynamics without Score Information, by Corentin Guichaoua, Paul Lascabettes and Elaine Chew.
https://doi.org/10.1177/20592043241233411
Revealing Footprints of Ancient Sources in Recent Eurasian and American Folk Music Cultures Using PCA of the Culture-Dependent Moment Vectors of Shared Melody Types, by Zoltán Juhász.
https://doi.org/10.1177/20592043241228982
Melodic Differences Between Styles: Modeling Music With Step Inertia, by Matt Chiu and David Temperley.
https://doi.org/10.1177/20592043231225731
Mel2Word: A Text-Based Melody Representation for Symbolic Music Analysis, by Saebyul Park, Eunjin Choi, Jeounghoon Kim and Juhan Nam.
https://doi.org/10.1177/20592043231216254
Understanding Feature Importance in Musical Works: Unpacking Predictive Contributions to Cluster Analyses, by Cameron J. Anderson and Michael Schutz.
https://doi.org/10.1177/20592043231216257
We hope that collection will provide a useful resource for researchers interested in using computational methods to advance our understanding of music.
Kind regards,
David Meredith
