Staff profile

Intro ... in the third person ;)

Mark Gotham specialises in computational methods for music theory, analysis, and composition. He holds the rare distinction of being appointed to permanent faculty positions in both STEM (now, at Durham) and in the humanities (previously Professor of Music Theory at T. U. Dortmund). Mark had held several other posts (in the UK, US, and Germany) and has degrees from Cambridge (PhD), the RNCM (MMus), and Oxford (BA, graduating with the Gibbs prize for the highest-ranking first class degree in the subject).

Research

Computer-aided methods can help not only to advance what we know about (a wide range of) musical structures, but also what we can do with that knowledge, and who can access it. Central to much of this is the encoding of human analyses in computer-readable formats.

A short list of recent publications is provided below (indexed on this webage from Durham's internal listings). For a more complete list see any of:

• ORCID,
• Google Scholar,
• Semantic Scholar,
• Web of Science

... And more

Beyond core research, my work has inluded commercial contracts, musical composition, and more.

• Notable commercial contracts have included the “Beethoven X” project for Deutsche Telekom: a commission to create some kind of realisation of Beethoven’s plans for a 10th symphony using AI (click here for further links).
• Earlier in my career, I was a highly active as a performer (singer, multi-instrumentalist, and conductor). Highlights included conducting principal players of the LSO and Philharmonia Orchestra in contemporary music projects. (Those days of active performance sure look like they're are over, though!)
• Composition highlights include broadcasts on BBC Radio 3 and national Chinese television, as well as a debut CD of compositions (‘Utrumne est Ornatum’, REGCD485) that attracted positive critical reviews, (e.g., with the Choir and Organ and Organists’ Review magazines awarding 5-stars).

Reach out!

I am an enthusiastic collaborator, regularly working with scholars (and musicians) at all career stages and all around the world. Prospective students are especially welcome to reach out at any time.

• Computational approached to music: composition, theory, and analysis;
• Computational/Digital humanities;
• Corpus creation and study;
• Mathematical modelling;
• Wider access, outreach, pedagogical/public-facing resources

Conference Paper

• Johnson, M., & Gotham, M. (2023). Musical Micro-Timing for Live Coding. In Proceedings of the 24th conference of the International Society for Music Information Retrieval (98-105). https://doi.org/10.5281/zenodo.10265231
• Gotham, M., Hentschel, J., Couturier, L., Dykeaylen, N., Rohrmeier, M., & Giraud, M. (2023). The ‘Measure Map’: an inter-operable standard for aligning symbolic music. In DLfM '23: Proceedings of the 10th International Conference on Digital Libraries for Musicology (91-99). https://doi.org/10.1145/3625135.3625136
• Gotham, M., Redbond, M., Bower, B., & Jonas, P. (2023). The “OpenScore String Quartet” Corpus. In M. E. Thomae (Ed.), DLfM '23: Proceedings of the 10th International Conference on Digital Libraries for Musicology (49-57). https://doi.org/10.1145/3625135.3625155
• Gotham, M. (2023). Chromatic Chords in Theory and Practice. In Proceedings of the 24th International Society for Music Information Retrieval Conference (272-278). https://doi.org/10.5281/zenodo.10265275

Conference Proceeding

• Gotham, M., Kleinertz, R., Weiß, C., Müller, M., & Klauk, S. (2021). What if the 'When' Implies the 'What'?: Human harmonic analysis datasets clarify the relative role of the separate steps in automatic tonal analysis. In J. H. Lee, A. Lerch, Z. Duan, J. Nam, P. Rao, P. V. Kranenburg, & A. Srinivasamurthy (Eds.),
• López, N. N., Gotham, M., & Fujinaga, I. (2021). Augmentednet: A Roman Numeral Analysis Network with Synthetic Training Examples and Additional Tonal Tasks.
• Gotham, M., & Yust, J. (2021). Serial Analysis: A Digital Library of Rows in the Repertoire and their Properties, with Applications for Teaching and Research. . https://doi.org/10.1145/3469013.3469018
• Liang, F., Gotham, M., Johnson, M., & Shotton, J. (2017). Automatic stylistic composition of Bach chorales with deep LSTM.

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
• 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
• Micchi, G., Gotham, M., & Giraud, M. (2020). Not All Roads Lead to Rome: Pitch Representation and Model Architecture for Automatic Harmonic Analysis. Transactions of the International Society for Music Information Retrieval, 3, 42 – 54. https://doi.org/10.5334/tismir.45
• Gotham, M. R. H. (2018). attractor Tempi in Brahms's Symphony No. 2/III. Music Theory Spectrum, 40(1), 138 – 153. https://doi.org/10.1093/mts/mty010
• Gotham, M. (2017). Hierarchy and position usage in ‘mixed’ metrical structures. Journal of New Music Research, 46, 103 – 117. https://doi.org/10.1080/09298215.2016.1253752
• Gotham, M. (2015). Attractor tempos for metrical structures. Journal of Mathematics and Music, 9, 23 – 44. https://doi.org/10.1080/17459737.2014.980343
• Gotham, M. (2014). First impressions: On the programming and concert presentation of new music today. Tempo, 68, 42 – 50. https://doi.org/10.1017/S0040298213001320

Other (Print)

• Vatolkin, I., Gotham, M., Lόpez, N. N., & Ostermann, F. (2023). Musical Genre Recognition Based on Deep Descriptors of Harmony, Instrumentation, and Segments. In C. Johnson, N. Rodríguez-Fernández, & S. M. Rebelo (Eds.), Artificial Intelligence in Music, Sound, Art and Design (413-427). https://doi.org/10.1007/978-3-031-29956-8_27