UCL Anthropology



Articles in Journals

  • J. Sterne. (2022). ‘Is Machine Listening Listening?‘ Communication +1 9:1. https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1111&context=cpo
  • J. Sterne, M. Sawhney. (2022). ‘The Acousmatic Question and the Will to Datafy: Otter.ai, Low-Resource Languages, and the Politics of Machine Listening.‘ Kalfou: A Journal of Comparative and Relational Ethnic Studies 8:1-2. 288-306. https://doi.org/10.7275/zeqh-eg38
  • L.  Michiels, R. Verachtert, A.  Ferraro, K. Falk, B. Goethals. (2023). ‘A Framework and Toolkit for Testing the Correctness of Recommendation Algorithms.’ ACM Transactions on Recommender Systems
  • M. Kischinhevsky, G. Ferreira, Í. Maduel Vieira. (2023). ’Serendipity on radio and streaming: Between musical discovery and recognition.’ New Media & Society, Online First. https://doi.org/10.1177/14614448231154568
  • S. Latifi, D. Jannach, A. Ferraro. ‘Sequential recommendation: A study on transformers, nearest neighbors and sampled metrics.‘ Information Sciences 609 (2022): 660-678. https://doi.org/10.1016/j.ins.2022.07.079
  • G. Vigliensoni, L. McCallum, E. Maestre, R. Fiebrink. (2022). ‘R-VAE: Live latent space drum rhythm generation from minimal-size datasets’. Journal of Creative Music Systems 1(1). https://doi.org/10.5920/jcms.902


Books and Monographs

  • N. Seaver. (2022). ‘Computing Taste: Algorithms and the Makers of Music Recommendation.’ University of Chicago Press.


Conference Papers

  • A. Ferraro, G. Ferreira, F. Diaz, G. Born. (2022). ‘Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship’. In Proceedings of the 16th ACM Conference on Recommender Systems. https://doi.org/10.1145/3523227.3551476
  • O. Bown. (2023). Music AI’s Potential Impact: Scoping the terms of the debate about value. AIMC 2023. Retrieved from https://aimc2023.pubpub.org/pub/rwi3v7tb
  • F. Diaz, A. Ferraro. (2022). ‘Offline Retrieval Evaluation Without Evaluation Metrics’. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.48550/arXiv.2204.11400
  • G. Vigliensoni, P. Perry, R. Fiebrink, R. (2022). ‘A small-data mindset for generative AI creative work’. In CHI’22 Workshop on Generative AI and HCI.
  • H. Wu, B. Mitra, C. Ma, F. Diaz, X. Liu. (2022). ‘Joint Multisided Exposure Fairness for Recommendation’. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://arxiv.org/abs/2204.11400
  • J. Shimizu, R. Fiebrink. (2023). ‘Genny: Designing and Exploring a Live Coding Interface for Generative Models’. In Proceedings of the International Conference on Live Codinghttps://doi.org/10.5281/zenodo.7843500
  • N. Thelle, R. Fiebrink. ‘How do musicians experience jamming with a co-creative ‘AI’?’ (2022). NeurIPS 2022 Workshop on Machine Learning for Creativity and Design.
  • P. Knees, A. Ferraro, M. Hüble. (2022). ‘Bias and Feedback Loops in Music Recommendation: Studies on Record Label Impact.‘ In H. Abdollahpouri, S. Sahebi, M. Elahi, M. Mansoury, B. Loni, Z. Nazari, & M. Dimakopoulou (Eds.), MORS 2022. Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems, co-located with 16th ACM Conference on Recommender Systems (RecSys 2022). CEUR-WS.org. https://doi.org/10.34726/3723
  • R. Srinivasan, E. Denton, J. Famularo, N. Rostamzadeh, F. Diaz, B. Coleman. (2021). ‘Artsheets for Art Datasets’. Thirty-fifth Conference on Neural Information Processing Systems https://research.google/pubs/pub51056/
  • R. Ocampo, J. Andres, C. Pegram, J. Shave, C. Hill, B. Wright, O. Bown. (2023) ‘Using GPT-3 to achieve semantically relevant data sonificiation for an art installation.‘ In EvoMUSART 2023, LNCS 13988 proceedings, book title Artificial Intelligence in Music, Sound, Art and Design, Springer Nature. https://doi.org/10.1007/978-3-031-29956-8_14
  • R. Ocampo, O. Bown, K. Grace. (2022). ‘A Framework for Dialogue-Based Human-AI Creative Collaboration.‘ Workshop on Generative HCI. ACM CHI.
  • S.  Makri, D. McKay, G. Buchanan, S. Chang, D. Lewandowski, A. MacFarlane, L. Cole, S. Vrijenhoek, and A. Ferraro. ‘Search a great leveler? Ensuring more equitable information acquisition.‘ Proceedings of the Association for Information Science and Technology 58, no. 1 (2021): 613-618. https://doi.org/10.1002/pra2.511
  • T. Carpentier, A. Einbond. (2022). ‘Spherical correlation as a similarity measure for 3D radiation patterns of musical instruments.‘ Proceedings of the 16th French Acoustics Conference, Marseille, France. https://hal.science/hal-03649913/file/carpentier-correlation-cfa-2022.pdf