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Kaila, A.-K. & Sturm, B. (2024). Agonistic Dialogue on the Value and Impact of AI Music Applications. In: Proceedings of the 2024 International Conference on AI and Musical Creativity: . Paper presented at 2024 International Conference on AI and Musical Creativity, 9 - 11 September, The University of Oxford. UK. Oxford, UK
Open this publication in new window or tab >>Agonistic Dialogue on the Value and Impact of AI Music Applications
2024 (English)In: Proceedings of the 2024 International Conference on AI and Musical Creativity, Oxford, UK, 2024Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we use critical and agonistic modes of inquiry to analyse and critique a specific application of AI to music practice. It records a structured interdisciplinary dialogue between 1) a musicologist and social scientist and 2) an engineer in music and computer science, focusing on folk-rnn and Irish Traditional Music (ITM) as a case study. We debate the role of data ethics in AI music applications, the dynamics of inclusion and exclusion, and the nature of embedded value systems and power asymmetries inherent in applying AI to music. We discuss how identifying the value of AI music applications is critical for ensuring research efforts make musical contributions along with academic and technical ones. Overall, this agonistic dialogue exemplifies how questions of right and wrong — the core of ethics — can be examined as AI is applied more and more to music practice.

Place, publisher, year, edition, pages
Oxford, UK: , 2024
Keywords
AI music, Irish Traditional Music, ethics, interdisciplinary, agonistic dialogue
National Category
Music
Research subject
Art, Technology and Design
Identifiers
urn:nbn:se:kth:diva-346695 (URN)
Conference
2024 International Conference on AI and Musical Creativity, 9 - 11 September, The University of Oxford. UK
Funder
Marianne and Marcus Wallenberg Foundation, 2020.0102EU, Horizon 2020, 864189
Note

QC 20240523

Available from: 2024-05-22 Created: 2024-05-22 Last updated: 2024-05-23Bibliographically approved
Amerotti, M., Benford, S., Sturm, B. & Vear, C. (2023). A Live Performance Rule System Informed by Irish Traditional Dance Music. In: Proc. International Symposium on Computer Music Multidisciplinary Research: . Paper presented at International Symposium on Computer Music Multidisciplinary Research.
Open this publication in new window or tab >>A Live Performance Rule System Informed by Irish Traditional Dance Music
2023 (English)In: Proc. International Symposium on Computer Music Multidisciplinary Research, 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes ongoing work in programming a live performance system for interpreting melodies in ways that mimic Irish traditional dance music practice, and thatallows plug and play human interaction. Existing performance systemsare almost exclusively aimed at piano performance and classical music, and noneare aimed specifically at traditional music.We develop a rule-based approach using expert knowledgethat converts a melody into control parametersto synthesize an expressive MIDI performance,focusing on ornamentation, dynamics and subtle time deviation.Furthermore, we make the system controllable (e.g., via knobs or expression pedals) such that it can be controlled in real time by a musician.Our preliminary evaluations show the systemcan render expressive performances mimicking traditional practice, and allows for engaging withIrish traditional dance music in new ways. We provide several examples online.

Keywords
Music performance modeling, traditional music, Irish
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-335907 (URN)
Conference
International Symposium on Computer Music Multidisciplinary Research
Funder
EU, Horizon 2020, 864189
Note

QC 20231123

Available from: 2023-09-09 Created: 2023-09-09 Last updated: 2023-11-23Bibliographically approved
Sturm, B. & Flexer, A. (2023). A Review of Validity and its Relationship to Music Information Research. In: Proc. Int. Symp. Music Information Retrieval: . Paper presented at 24th International Society for Music Information Retrieval Conference, Milan, Italy, 5 - 9 November, 2023.
Open this publication in new window or tab >>A Review of Validity and its Relationship to Music Information Research
2023 (English)In: Proc. Int. Symp. Music Information Retrieval, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Validity is the truth of an inference made from evidence and is a central concern in scientific work. Given the maturity of the domain of music information research (MIR), validity in our opinion should be discussed and considered much more than it has been so far. Puzzling MIR phenomena like adversarial attacks, horses, and performance glass ceilings become less mysterious through the lens of validity. In this paper, we review the subject of validity as presented in a key reference of causal inference: Shadish et al., "Experimental and Quasi-experimental Designs for Generalised Causal Inference". We discuss the four types of validity and threats to each one. We consider them in relationship to MIR experiments grounded with a practical demonstration using a typical MIR experiment. 

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-335908 (URN)
Conference
24th International Society for Music Information Retrieval Conference, Milan, Italy, 5 - 9 November, 2023
Funder
EU, Horizon 2020, 864189
Note

QC 20231006

Available from: 2023-09-09 Created: 2023-09-09 Last updated: 2023-10-06Bibliographically approved
Falk, S., Sturm, B. & Ahlbäck, S. (2023). Automatic legato transcription based on onset detection. In: SMC 2023: Proceedings of the Sound and Music Computing Conference 2023. Paper presented at 20th Sound and Music Computing Conference, SMC 2023, Hybrid, Stockholm, Sweden, Jun 15 2023 - Jun 17 2023 (pp. 214-221). Sound and Music Computing Network
Open this publication in new window or tab >>Automatic legato transcription based on onset detection
2023 (English)In: SMC 2023: Proceedings of the Sound and Music Computing Conference 2023, Sound and Music Computing Network , 2023, p. 214-221Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on the transcription of performance expression and in particular, legato slurs for solo violin performance. This can be used to improve automatic music transcription and enrich the resulting notations with expression markings. We review past work in expression detection, and find that while legato detection has been explored its transcription has not. We propose a method for demarcating the beginning and ending of slurs in a performance by combining pitch and onset information produced by ScoreCloud (a music notation software with transcription capabilities) with articulated onsets detected by a convolutional neural network. To train this system, we build a dataset of solo bowed violin performance featuring three different musicians playing several exercises and tunes. We test the resulting method on a small collection of recordings of the same excerpt of music performed by five different musicians. We find that this signal-based method works well in cases where the acoustic conditions do not interfere largely with the onset strengths. Further work will explore data augmentation for making the articulation detection more robust, as well as an end-to-end solution. 

Place, publisher, year, edition, pages
Sound and Music Computing Network, 2023
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-327112 (URN)2-s2.0-85171797881 (Scopus ID)
Conference
20th Sound and Music Computing Conference, SMC 2023, Hybrid, Stockholm, Sweden, Jun 15 2023 - Jun 17 2023
Funder
EU, Horizon 2020, 864189
Note

Part of ISBN 9789152773727

QC 20230525

Available from: 2023-05-19 Created: 2023-05-19 Last updated: 2024-01-09Bibliographically approved
Huang, R., Holzapfel, A., Sturm, B. & Kaila, A.-K. (2023). Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music. Transactions of the International Society for Music Information Retrieval, 6(1), 43-59
Open this publication in new window or tab >>Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music
2023 (English)In: Transactions of the International Society for Music Information Retrieval, E-ISSN 2514-3298, Vol. 6, no 1, p. 43-59Article in journal (Refereed) Published
Abstract [en]

Musical worlds, not unlike our lived realities, are fundamentally fragmented and diverse, a fact often seen as a challenge or even a threat to the validity of research in Music Information Research (MIR). In this article, we propose to treat this characteristic of our musical universe(s) as an opportunity to fundamentally enrich and re-orient MIR. We propose that the time has arrived for MIR to reflect on its ethical and cultural turns (if they have been initiated at all) and take them a step further, with the goal of profoundly diversifying the discipline beyond the diversification of datasets. Such diversification, we argue, is likely to remain superficial if it is not accompanied by a simultaneous auto-critique of the discipline’s raison d’être. Indeed, this move to diversify touches on the philosophical underpinnings of what MIR is and should become as a field of research: What is music (ontology)? What are the nature and limits of knowledge concerning music (epistemology)? How do we obtain such knowledge (methodology)? And what about music and our own research endeavor do we consider “good” and “valuable” (axiology)? This path involves sincere inter- and intra-disciplinary struggles that underlie MIR, and we point to “agonistic interdisciplinarity” — that we have practiced ourselves via the writing of this article — as a future worth reaching for. The two featured case studies, about possible philosophical re-orientations in approaching ethics of music AI and about responsible engineering when AI meets traditional music, indicate a glimpse of what is possible.

Place, publisher, year, edition, pages
Ubiquity Press, Ltd., 2023
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-326016 (URN)10.5334/tismir.141 (DOI)2-s2.0-85165911770 (Scopus ID)
Funder
EU, Horizon 2020, 864189Marianne and Marcus Wallenberg Foundation, 2020.0102Swedish Research Council, 2019-03694
Note

QC 20230425

Available from: 2023-04-21 Created: 2023-04-21 Last updated: 2024-02-13Bibliographically approved
Déguernel, K. & Sturm, B. (2023). Bias in Favour or Against Computational Creativity: A Survey and Reflection on the Importance of Socio-cultural Context in its Evaluation. In: Proc. International Conference on Computational Creativity: . Paper presented at International Conference on Computational Creativity.
Open this publication in new window or tab >>Bias in Favour or Against Computational Creativity: A Survey and Reflection on the Importance of Socio-cultural Context in its Evaluation
2023 (English)In: Proc. International Conference on Computational Creativity, 2023Conference paper, Published paper (Refereed)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-327091 (URN)
Conference
International Conference on Computational Creativity
Funder
EU, Horizon 2020, 864189
Note

QC 20230522

Available from: 2023-05-18 Created: 2023-05-18 Last updated: 2023-05-22Bibliographically approved
Cros Vila, L. & Sturm, B. (2023). Statistical evaluation of abc-formatted music at the levels of items and corpora. In: Proc. AI Music Creativity Conference: . Paper presented at AI Music Creativity.
Open this publication in new window or tab >>Statistical evaluation of abc-formatted music at the levels of items and corpora
2023 (English)In: Proc. AI Music Creativity Conference, 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper explores three distance measures and three statistical tests for the comparison of music expressed in abc format. We propose a methodology that allows for an analysis at the level of corpora (is the “style” represented in a corpus the same as that in the another corpus?) as well as at the level of item (is the “style” of an item that of the “style” represented in a corpus?). We estimate distributions of distances between item pairs within and between corpora, and test hypotheses that the distributions are identical. We empirically test the impact of distance measure and statistical test using a corpus of Irish traditional dance music and a collection of tunes generated by a machine learning model trained on the same. The proposed methodology has a variety of applications, from computational musicology, to evaluating machine generated music.

National Category
Musicology
Identifiers
urn:nbn:se:kth:diva-335904 (URN)
Conference
AI Music Creativity
Funder
EU, Horizon 2020, 864189
Note

QC 20230911

Available from: 2023-09-09 Created: 2023-09-09 Last updated: 2023-09-11Bibliographically approved
Sturm, B. (2023). The Ai Music Generation Challenge 2022: Summary and Results. In: Proc. AI Music Creativity Conference: . Paper presented at AI Music Creativity.
Open this publication in new window or tab >>The Ai Music Generation Challenge 2022: Summary and Results
2023 (English)In: Proc. AI Music Creativity Conference, 2023Conference paper, Published paper (Refereed)
Abstract [en]

We discuss the design and results of The Ai Music Generation Challenge 2022 and compare it to the previous two challenges. While the 2020 challenge focused on generating Irish double jigs, and the 2021 challenge focused on generating Swedish slängpolskor, the 2022 challenge posed three sub-challenges in the context of Irish traditional music: generation of reels, judging tune submissions, and titling tunes. In total seven systems participated in the sub-challenges, along with benchmark systems. One tune was awarded first prize by the judges, and two tunes shared second prize. A submitted system for judging tunes clearly performed better than two benchmarks. Finally, human tune-titling outperformed the benchmark and submitted system, but gave rise to some interesting issues about tune titling.

National Category
Media Engineering
Identifiers
urn:nbn:se:kth:diva-335903 (URN)
Conference
AI Music Creativity
Funder
EU, Horizon 2020, 864189
Note

QC 20230911

Available from: 2023-09-09 Created: 2023-09-09 Last updated: 2023-09-11Bibliographically approved
Sturm, B., Uitdenbogerd, A. L., Koops, H. V. & Huang, A. (2022). Editorial for TISMIR Special Collection: AI and Musical Creativity. Transactions of the International Society for Music Information Retrieval, 5(1), 67-70
Open this publication in new window or tab >>Editorial for TISMIR Special Collection: AI and Musical Creativity
2022 (English)In: Transactions of the International Society for Music Information Retrieval, ISSN 2514-3298, Vol. 5, no 1, p. 67-70Article in journal, Editorial material (Other academic) Published
Abstract [en]

This special issue focuses on research developments and critical thought in the domain of artificial intelligence (AI) applied to modeling and creating music. It is motivated by the AI Song Contests of 2020 and 2021, in which the four guest editors adjudicated or participated among many teams from around the world. The 2020 edition had 13 submissions and the 2021 edition had 38. The 2022 edition is now being planned. These unique events provide exciting opportunities for AI music researchers to test the state of the art and push the boundaries of what is possible, within the context of music creation. They portend a future when humans and machines work together as partners in music creation. Maybe "portend" is not the right term, but we must not think that the future of AI and music is only warm and fuzzy. It is important and timely to consider how we, in local and global contexts, can effectively and ethically develop and apply AI in contexts of music creation.

Place, publisher, year, edition, pages
Ubiquity Press, Ltd., 2022
Keywords
Artificial intelligence, creativity, ethics, music generation
National Category
Musicology Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-324728 (URN)10.5334/tismir.129 (DOI)2-s2.0-85132113940 (Scopus ID)
Note

QC 20230612

Available from: 2023-03-15 Created: 2023-03-15 Last updated: 2023-06-12Bibliographically approved
Sturm, B. (2022). Generative AI helps one express things for which they may not have expressions (yet). In: Proc. Generative AI and HCI Workshop at CHI: . Paper presented at Generative AI and HCI Workshop at CHI.
Open this publication in new window or tab >>Generative AI helps one express things for which they may not have expressions (yet)
2022 (English)In: Proc. Generative AI and HCI Workshop at CHI, 2022Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-327111 (URN)
Conference
Generative AI and HCI Workshop at CHI
Funder
EU, Horizon 2020, 864189
Note

QC 20230525

Available from: 2023-05-19 Created: 2023-05-19 Last updated: 2023-05-25Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2549-6367

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