UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Automated manipulation of musical grammars to support episodic interactive experiences

Aspromallis, Christodoulos; (2020) Automated manipulation of musical grammars to support episodic interactive experiences. Doctoral thesis (Ph.D), UCL (University College London). Green open access

[thumbnail of Aspromallis_ID_thesis-EDITED COPY.pdf]
Preview
Text
Aspromallis_ID_thesis-EDITED COPY.pdf

Download (40MB) | Preview

Abstract

Music is used to enhance the experience of participants and visitors in a range of settings including theatre, film, video games, installations and theme parks. These experiences may be interactive, contrastingly episodic and with variable duration. Hence, the musical accompaniment needs to be dynamic and to transition between contrasting music passages. In these contexts, computer generation of music may be necessary for practical reasons including distribution and cost. Automated and dynamic composition algorithms exist but are not well-suited to a highly interactive episodic context owing to transition-related problems including discontinuity, abruptness, extended repetitiveness and lack of musical granularity and musical form. Addressing these problems requires algorithms capable of reacting to participant behaviour and episodic change in order to generate formic music that is continuous and coherent during transitions. This thesis presents the Form-Aware Transitioning and Recovering Algorithm (FATRA) for realtime, adaptive, form-aware music generation to provide continuous musical accompaniment in episodic context. FATRA combines stochastic grammar adaptation and grammar merging in real time. The Form-Aware Transition Engine (FATE) implementation of FATRA estimates the time-occurrence of upcoming narrative transitions and generates a harmonic sequence as narrative accompaniment with a focus on coherent, form-aware music transitioning between music passages of contrasting character. Using FATE, FATRA has been evaluated in three perceptual user studies: An audioaugmented real museum experience, a computer-simulated museum experience and a music-focused online study detached from narrative. Music transitions of FATRA were benchmarked against common approaches of the video game industry, i.e. crossfading and direct transitions. The participants were overall content with the music of FATE during their experience. Transitions of FATE were significantly favoured against the crossfading benchmark and competitive against the direct transitions benchmark, without statistical significance for the latter comparison. In addition, technical evaluation demonstrated capabilities of FATRA including form generation, repetitiveness avoidance and style/form recovery in case of falsely predicted narrative transitions. Technical results along with perceptual preference and competitiveness against the benchmark approaches are deemed as positive and the structural advantages of FATRA, including form-aware transitioning, carry considerable potential for future research.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Automated manipulation of musical grammars to support episodic interactive experiences
Event: UCL
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10091027
Downloads since deposit
56Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item