Outputs (7)

When in Rome: A Meta-corpus of Functional Harmony (2023)
Journal Article
Gotham, M., Micchi, G., López, N. N., & Sailor, M. (2023). When in Rome: A Meta-corpus of Functional Harmony. Transactions of the International Society for Music Information Retrieval, 6(1), 150-166. https://doi.org/10.5334/tismir.165

‘When in Rome’ brings together all human-made, computer-encoded, functional harmonic analyses of music. This amounts in total to over 2,000 analyses of 1,500 distinct works. The most obvious motivation is scale: gathering these datasets together lead... Read More about When in Rome: A Meta-corpus of Functional Harmony.

The “OpenScore String Quartet” Corpus (2023)
Presentation / Conference Contribution
Gotham, M., Redbond, M., Bower, B., & Jonas, P. (2023, November). The “OpenScore String Quartet” Corpus. Presented at DLfM 2023: 10th International Conference on Digital Libraries for Musicology, Milan, Italy

The “OpenScore String Quartet” Corpus is a new dataset of historic works for string quartets, encoded by a dedicated team of volunteers, and released freely for all use cases (CC0). In creating this corpus, we built on the experience amassed during t... Read More about The “OpenScore String Quartet” Corpus.

The 'Measure Map': An inter-operable standard for aligning symbolic music (2023)
Presentation / Conference Contribution
Gotham, M., Hentschel, J., Couturier, L., Dykeaylen, N., Rohrmeier, M., & Giraud, M. (2023, November). The 'Measure Map': An inter-operable standard for aligning symbolic music. Presented at DLfM 2023: 10th International Conference on Digital Libraries for Musicology, Milan, Italy

Aligning versions of the same source material has been a persistent challenge in the field of digital libraries for musicology, and a barrier to progress. The growing number of publicly accessible symbolic datasets (of scores, analyses, and more) now... Read More about The 'Measure Map': An inter-operable standard for aligning symbolic music.

Chromatic Chords in Theory and Practice (2023)
Presentation / Conference Contribution
Gotham, M. (2023, November). Chromatic Chords in Theory and Practice. Presented at 24th International Society for Music Information Retrieval Conference, Milan, Italy

"Chromatic harmony" is seen as a fundamental part of (extended) tonal music in the Western classical tradition (c.1700–1900). It routinely features in core curricula. Yet even in this globalised and data-driven age, 1) there are significant gaps betw... Read More about Chromatic Chords in Theory and Practice.

Musical Micro-Timing for Live Coding (2023)
Presentation / Conference Contribution
Johnson, M., & Gotham, M. (2023, November). Musical Micro-Timing for Live Coding. Presented at ISMIR 2023: 4th conference of the International Society for Music Information Retrieval, Milan, Italy

Micro-timing is an essential part of human music-making, yet it is absent from most computer music systems. Partly to address this gap, we present a novel system for generating music with style-specific micro-timing within the Sonic Pi live coding la... Read More about Musical Micro-Timing for Live Coding.

Old Sources in new Sauces: John Joubert and the Analysis of Ancient Materials in Modern Music (2023)
Journal Article
Gotham, M. (2023). Old Sources in new Sauces: John Joubert and the Analysis of Ancient Materials in Modern Music. Intégral: The Journal of Applied Musical Thought, 36, 153-162

Anyone who has had sustained contact with Anglican choirs probably knows music by John Joubert; anyone who hasn’t probably doesn’t. This article begins by considering the starkness of those divisions between different kinds of canons, framing this in... Read More about Old Sources in new Sauces: John Joubert and the Analysis of Ancient Materials in Modern Music.

Musical Genre Recognition Based on Deep Descriptors of Harmony, Instrumentation, and Segments (2023)
Presentation / Conference Contribution
Vatolkin, I., Gotham, M., Lόpez, N. N., & Ostermann, F. (2023, April). Musical Genre Recognition Based on Deep Descriptors of Harmony, Instrumentation, and Segments. Presented at EvoMUSART 2023: Artificial Intelligence in Music, Sound, Art and Design, Brno, Czech Republic

Deep learning has recently established itself as a cluster of methods of choice for almost all classification tasks in music information retrieval. However, despite very good classification performance, it sometimes brings disadvantages including lon... Read More about Musical Genre Recognition Based on Deep Descriptors of Harmony, Instrumentation, and Segments.