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Publications

Articles in Journals

  • Baym, N., Bergmann, R., Bhargava, R., Diaz, F., Gillespie, T., Hesmondhalgh, D., Maris, E., & Persaud, C. J. (2021). ‘Making Sense of Metrics in the Music Industries’International Journal of Communication (Online), 3418-.
  • Bown, O. (2024). ‘Blind search and flexible product visions: the sociotechnical shaping of generative music engines
  • Carpentier, T., & Einbond, A. (2023). ‘Spherical correlation as a similarity measure for 3-D radiation patterns of musical instruments‘. Acta Acustica7(40), 40-. https://doi.org/10.1051/aacus/2023033
  • Einbond, A., Carpentier, T., Schwarz, D., & Bresson, J. (2022). ‘Embodying Spatial Sound Synthesis with AI in Two Compositions for Instruments and 3-D Electronics‘. Computer Music Journal46(4), 1–40. https://doi.org/10.1162/comj_a_00664
  • Gioti, A.-M., Einbond, A., & Born, G. (2023). ‘Composing the Assemblage: Probing Aesthetic and Technical Dimensions of Artistic Creation with Machine Learning‘. Computer Music Journal pp. 1-43
  • Kischinhevsky, M., Ferreira, G., Maduel Vieira, Í.. (2023). ’Serendipity on radio and streaming: Between musical discovery and recognition.’ New Media & Society, Online First. https://doi.org/10.1177/14614448231154568
  • Latifi, S., Jannach, D., Ferraro, A. ‘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
  • Michiels, L., Verachtert, R., Ferraro, A., Falk, K. & Goethals, B. (2023). ‘A Framework and Toolkit for Testing the Correctness of Recommendation Algorithms.’ ACM Transactions on Recommender Systems
  • Sprengel, D. (2023). ‘Imperial lag: some spatial-temporal politics of music streaming’s global expansion’Communication, Culture & Critique16(4), 243–249. https://doi.org/10.1093/ccc/tcad024
  • Sterne, J. (2022). ‘Is Machine Listening Listening?‘ Communication +1 9:1. https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1111&context=cpo
  • Sterne J. & Sawhney, M. (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
  • Vigliensoni, G., McCallum, L., Maestre, E., Fiebrink, R. (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

  • Drott, E. (2024). ‘Streaming Music, Streaming Capital’ Duke University Press

 

Conference Papers

  • Bown, O. (2023). ‘Music AI’s Potential Impact: Scoping the terms of the debate about value‘. AIMC 2023. Retrieved from https://aimc2023.pubpub.org/pub/rwi3v7tb
  • Carpentier, T., Einbond, A. (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
  • Diaz, F. (2023). ‘Best-Case Retrieval Evaluation: Improving the Sensitivity of Reciprocal Rank with Lexicographic Precision‘. arXiv.Orghttps://doi.org/10.48550/arxiv.2306.07908
  • Diaz, F., Ferraro, A. (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
  • Ekstrand, M. D., Carterette, B., & Diaz, F. (2023). ‘Distributionally-Informed Recommender System Evaluation‘. arXiv.Orghttps://doi.org/10.48550/arxiv.2309.05892
  • Ekstrand, M. D., Das, A., Burke, R., & Diaz, F. (2022). ‘Fairness in Information Access Systems‘. arXiv.Orghttps://doi.org/10.48550/arxiv.2105.05779
  • Ferraro, A., Ferreira, G., Diaz, F., Born, G.. (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
  • Gutiérrez, E., Haworth, C., & Cádiz, R. (2023). ‘Generating Quadratic Difference Tone Spectra for Auditory Distortion Synthesis‘. In ICMC 2023: The sound of changes: International Computer Music Conference Proceedings (pp. 237-243). International Computer Music Association.
  • Knees, P., Ferraro, A, Hüble, M. (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
  • Ma, A., Patitsas, E. & Sterne, J. (2023) ‘You Sound Depressed: A Case Study on Sonde Health’s Diagnostic Use of Voice Analysis AI‘. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23). Association for Computing Machinery, New York, NY, USA, 639–650. https://doi.org/10.1145/3593013.3594032
  • Makri, S., McKay, D., Buchanan, G., Chang, S., Lewandowski, D., MacFarlane, A., Cole, L. Vrijenhoek, S., and Ferraro, A. ‘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
  • Ocampo, R., Andres, J., Pegram, C., Shave, J., Hill, C., Wright, B., Bown, O. (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
  • Ocampo, R., Bown, O., Grace, K. (2022). ‘A Framework for Dialogue-Based Human-AI Creative Collaboration.‘ Workshop on Generative HCI. ACM CHI.
  • Shimizu, J., Fiebrink, R. (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
  • Srinivasan, R., Denton, E., Famularo, J., Rostamzadeh, N., Diaz, F., Coleman, B. (2021). ‘Artsheets for Art Datasets’. Thirty-fifth Conference on Neural Information Processing Systems https://research.google/pubs/pub51056/
  • Thelle, N., Fiebrink, R. ‘How do musicians experience jamming with a co-creative ‘AI’?’ (2022). NeurIPS 2022 Workshop on Machine Learning for Creativity and Design.
  • Wu, H., Mitra, B., Ma, C., Diaz, F. Liu, X. (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
  • Vigliensoni, G., Perry, P., Fiebrink, R. (2022). ‘A small-data mindset for generative AI creative work’. In CHI’22 Workshop on Generative AI and HCI.

 

Edited Journal Issues

  • Déguernel, K., Sturm,  Bob L., Gioti, A.-M., & Born, G. (2022) ‘Musical Interactivity in Human–AI and AI–AI Partnerships‘. Computer Music Journal 2022; 46 (4): 5–6. doi: https://doi.org/10.1162/comj_e_00656