Integrative Research in Art and Science: A Framework for Proactive Humanities

Lesaffre, Micheline; Leman, Marc

doi:10.1080/02560046.2020.1788616

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…We have also observed correspondences between the onsets and offset of the finger flexors EMG and indicators of phrasing in the annotations. These findings support the notion that a more comprehensive analysis can be achieved through crossmodal processing and multimodal fusion methods on quantitative and qualitative data (Essid and Richard, 2012;Lesaffre and Leman, 2020). Further work on larger datasets is necessary, and we are therefore planning further data collection involving diverse instrumentalists and instruments.…”

Section: Discussion and Future Worksupporting

confidence: 76%

Method Development for Multimodal Data Corpus Analysis of Expressive Instrumental Music Performance

Visi

Östersjö

et al. 2020

Front. Psychol.

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Musical performance is a multimodal experience, for performers and listeners alike. This paper reports on a pilot study which constitutes the first step toward a comprehensive approach to the experience of music as performed. We aim at bridging the gap between qualitative and quantitative approaches, by combining methods for data collection. The purpose is to build a data corpus containing multimodal measures linked to high-level subjective observations. This will allow for a systematic inclusion of the knowledge of music professionals in an analytic framework, which synthesizes methods across established research disciplines. We outline the methods we are currently developing for the creation of a multimodal data corpus dedicated to the analysis and exploration of instrumental music performance from the perspective of embodied music cognition. This will enable the study of the multiple facets of instrumental music performance in great detail, as well as lead to the development of music creation techniques that take advantage of the cross-modal relationships and higher-level qualities emerging from the analysis of this multi-layered, multimodal corpus. The results of the pilot project suggest that qualitative analysis through stimulated recall is an efficient method for generating higher-level understandings of musical performance. Furthermore, the results indicate several directions for further development, regarding observational movement analysis, and computational analysis of coarticulation, chunking, and movement qualities in musical performance. We argue that the development of methods for combining qualitative and quantitative data are required to fully understand expressive musical performance, especially in a broader scenario in which arts, humanities, and science are increasingly entangled. The future work in the project will therefore entail an increasingly multimodal analysis, aiming to become as holistic as is music in performance.

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Section: Discussion and Future Worksupporting

confidence: 76%

Method Development for Multimodal Data Corpus Analysis of Expressive Instrumental Music Performance

Visi

Östersjö

et al. 2020

Front. Psychol.

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“…The total score of the work is excellent in the range of 90–100, good in the range of 80–89, average in the range of 70–79, qualified in the range of 60–69, and effort is needed in the range of 0–59. The design of piano fingering is closely related to the musical notes and piano keys, and the note information includes information such as pitch, pitch length, and pitch intensity, among which the pitch is associated with the piano keys; therefore, the pitch will be an important musical feature for the automatic generation of piano fingering research in this paper [ 9 ]. As piano fingering is closely related to the key position of the fingers, and the key position information represents the pitch information of the notes in the concrete form of piano playing, if we know the key position of each note on the piano keyboard, then we can follow the fingering rules to complete the performance of the music [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Study of Piano Timbre Teaching in the Context of Artificial Intelligence Interaction

Cui

2021

Computational Intelligence and Neuroscience

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This paper provides an in-depth analysis and research on piano timbre teaching in the context of artificial intelligence interaction, a bold vision of piano teaching, proposes a feasible solution in terms of teaching modules in intelligent piano teaching for senior teachers, and proposes an implementation path for the integration of intelligent piano and piano teaching from the four main blocks of piano teaching. Based on the multiplicative harmonic model of monophonic signal, combined with the variability of timbre characteristics, an audio synthesis model with editable timbre is proposed, and the experimental results show that editing the timbre parameters in the model can realize timbre modification, and the synthesized timbre conforms to the piano timbre characteristics. Based on the timbre analysis and the timbre synthesis model, a piano timbre library generation system is designed. The detailed design of the software modules such as audio file reading and writing, audio information analysis, timbre parameter acquisition, timbre synthesis, and simulated performance is given. The system can generate piano timbre libraries of different qualities flexibly and meet the requirements of timbre realism. The teaching experiment designed for teaching practice from solo teaching, and the practice target is first-year undergraduate students in the university, and the practice period is six weeks, and finally, the feasibility of intelligent piano teaching application is analysed by combining the experimental results. Through the teaching objectives, teaching content, and teaching methods, teaching environment reflects intelligent piano teaching to make up for the limitations of traditional piano teaching. Analyse the development trend of intelligent piano teaching in the context of artificial intelligence interaction, and explore the value of intelligent piano teaching.

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Gesture, Music and Computer: The Centro di Sonologia Computazionale at Padova University, a 50-Year History

Canazza

Poli

Vidolin

2022

Sensors

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With the advent of digital technologies, the computer has become a generalized tool for music production. Music can be seen as a creative form of human–human communication via a computer, and therefore, research on human–computer and computer–human interfaces is very important. This paper, for the Sensors Special Issue on 800 Years of Research at Padova University, presents a review of the research in the field of music technologies at Padova University by the Centro di Sonologia Computazionale (CSC), focusing on scientific, technological and musical aspects of interaction between musician and computer and between computer and audience. We discuss input devices for detecting information from gestures or audio signals and rendering systems for audience and user engagement. Moreover, we discuss a multilevel conceptual framework, which allows multimodal expressive content processing and coordination, which is important in art and music. Several paradigmatic musical works that stated new lines of both musical and scientific research are then presented in detail. The preservation of this heritage presents problems very different from those posed by traditional artworks. CSC is actively engaged in proposing new paradigms for the preservation of digital art.

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Integrative Research in Art and Science: A Framework for Proactive Humanities

Cited by 3 publications

References 44 publications

Method Development for Multimodal Data Corpus Analysis of Expressive Instrumental Music Performance

Method Development for Multimodal Data Corpus Analysis of Expressive Instrumental Music Performance

A Study of Piano Timbre Teaching in the Context of Artificial Intelligence Interaction

Gesture, Music and Computer: The Centro di Sonologia Computazionale at Padova University, a 50-Year History

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