From October 2020 – 2025, MUSAiC will analyze, criticize and fundamentally broaden the AI transformation of three interrelated music practices: 1) listening, 2) composition and performance, and 3) analysis and criticism. For each practice, and grounded in two specific music traditions (Irish and Swedish), MUSAiC will document and critically analyze the impacts of and ethical issues surrounding Ai. MUSAiC will formulate and implement the first music pedagogy for Ai, the lack of which continues to result in the creation of Ai systems that have only a surface knowledge of music. From this pedagogy, MUSAiC will develop new holistic methods for understanding and benchmarking Ai, and improving them and their application. It will implement and test novel Ai systems that dynamically adapt to specific users as “digital apprentices”, thus bringing human-Ai music partnerships to new levels of fruitfulness.
The outcomes of MUSAiC will facilitate applications of Ai to music in robust and responsible ways, impacting a wide variety of stakeholders. It will not only prepare music practitioners and audiences of the present (human and artificial) for new ways of listening, working, appraising, and developing the art form, but will also pave the way for analyzing, criticizing and broadening the transformation of the other Arts by Ai.
- Sturm, B. L., Santos, J. F., Ben-Tal, O., and Korshunova, I. (2016). Music transcription modelling and composition using deep learning. In Proc. Conf. Computer Simulation of Musical Creativity, Huddersfield, UK.
- Sturm, B. L. and Ben-Tal, O. (2017). Taking the models back to music practice: Evaluating generative transcription models built using deep learning. J. Creative Music Systems, 2(1).
- Sturm, B. L. and Ben-Tal, O. (2018). Let’s Have Another Gan Ainm: An experimental album of Irish traditional music and computer-generated tunes. Technical report, KTH Royal Institute of Technology.
- Sturm, B. L. (2018). How stuff works: LSTM model of folk music transcriptions. In Proc. Joint Workshop on Machine Learning for Music, ICML.
- Sturm, B. L. (2018). What do these 5,599,881 parameters mean? an analysis of a specific lstm music transcription model, starting with the 70,281 parameters of its softmax layer. In Proc. Music Metacreation workshop of ICCC.
- Holzapfel, A., Sturm, B. L., and Coeckelbergh, M. (2018). Ethical dimensions of music information retrieval technology. Trans. Int. Soc. Music Information Retrieval, 1(1): 44–55.
- Hallström, E., Mossmyr, S., Sturm, B. L., Vegeborn, V. H., and Wedin, J. (2019). From jigs and reels to schottisar och polskor: Generating Scandinavian-like folk music with deep recurrent networks. In Proc. Sound and Music Computing Conf.
- Lousseief, E. and Sturm, B. L. T. (2019). Mahlernet: Unbounded orchestral music with neural networks. In Proc. Nordic Sound and Music Computing.
- Sturm, B. L., Ben-Tal, O., Monaghan, U., Collins, N., Herremans, D., Chew, E., Hadjeres, G., Deruty, E., and Pachet, F. (2018). Machine learning research that matters for music creation: A case study. J. New Music Research, 48(1):36–55.
- Sturm, B. L., Iglesias, M., Ben-Tal, O., Miron, M., and Gómez, E. (2019). Artificial intelligence and music: Open questions of copyright law and engineering praxis. MDPI Arts, 8(3).
- Rodríguez-Algarra, F., Sturm, B. L., and Dixon, S. (2019). Characterising confounding effects in music classification experiments through interventions. Trans. Int. Soc. Music Information Retrieval, 2(1):52–66.
- Ben-Tal, O., Harris, M. T., and Sturm, B. L. T. (2020). How music Ai is useful: Engagements with composers, performers, and audiences. Leonardo.
- Jonason, N., Sturm, B. L. T., and Thomé, C. (2020). The control-synthesis approach for making expressive and controllable neural music synthesizers. In Proc. AI Music Creativity.
- Sturm, B. L. and Wiggins, G. (2021). “The mismeasure of music: On computerised music listening and analysis.” In Oxford Handbook of Music and Corpus Studies. Oxford University Press.
- Sturm, B. L. T. and Ben-Tal, O. (2021). “Folk the Algorithms: (Mis)Applying Artificial Intelligence to Folk Music”. In Handbook of Artificial Intelligence for Music, Springer.
MUSAiC is a project that has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 864189).