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 Acustica, 7(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 Journal, 46(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 & Critique, 16(4), 243–249. https://doi.org/10.1093/ccc/tcad024
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
Diaz, F. (2023). ‘Best-Case Retrieval Evaluation: Improving the Sensitivity of Reciprocal Rank with Lexicographic Precision‘. arXiv.Org. https://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
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 Coding. https://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
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