Hi list, [Apologies for cross posting]
Fully funded PhD opportunity at Edinburgh Napier University, researching DDSP in Procedural Audio Sound Effects. Full details https://blogs.napier.ac.uk/scebe-research/wp-content/uploads/sites/132/2023/10/Oct23-Ai-Fu-PhD-Selfridge-on-DDSP-in-Procedural-Audio.pdf Deadline – 3rd December 2023 Projects are anticipated to start on 1st October 2024. Apply via https://www.jobs.ac.uk/job/DDV112/fully-funded-phd-studentships-in-applied-informatics Informal contact to Dr Rod Selfridge – r.selfri...@napier.ac.uk ****************** Project description: This aim of this research is to improve the audio quality of synthesized procedural audio sound effects, optimising parameters through the use of differential digital signal processing (DDSP) techniques. Physically inspired synthesis techniques often used for procedural audio sound effects [1], where basic knowledge of the sound producing process and behaviour modelling are integrated within the synthesis process. Previous research has incorporated deeper knowledge of the physical processes to improve the quality of the sounds synthesised, but it is still possible for listeners to identify synthesised sounds when compared to the recorded samples [2]. DDSP covers a number of techniques where signal processors are integrated within neural networks [3]. Through backpropagation of loss functions, the signal processors can be optimised for specific synthesis models. One drawback of physically inspired procedural models is that potential critical aspects of the physical process as well as the behaviour model that controls the sound synthesis process can be missed. By training the parameters of the synthesis models using DDSP, based on pre-recorded samples, it should be possible to capture missing elements of the models, (behaviour etc), and apply these to new synthesis models. Similar separation of the sounds generated by a musical instrument has been carried out in [4] where the performance data is preserved while the timbre. The use of DDSP and neural networks for the purposes of sound effects is an ongoing area of research. DDSP has more recently be used to generate sound effects [5] or inspired vocalisation synthesis techniques [6], and different neural synthesis approaches to foley have also been explored [7, 8, 9, 10]. This research looks to build on this body of research, using DDSP to control new physically inspired sound effect models, to improve behaviour and plausibility, and ultimately the quality of synthesised sound effects. References: [1] Farnell, A. (2010). Designing sound. Mit Press. [2] Selfridge, R., Moffat, D., Avital, E. J., & Reiss, J. D. (2018). Creating real-time aeroacoustic sound effects using physically informed models. Journal of the Audio Engineering Society, 66(7/8), 594-607. [3] Hayes, B., Shier, J., Fazekas, G., McPherson, A., & Saitis, C. (2023). A Review of Differentiable Digital Signal Processing for Music & Speech Synthesis. arXiv preprint arXiv:2308.15422 [4] Dai, S., Zhang, Z., & Xia, G. G. (2018). Music style transfer: A position paper. arXiv preprint arXiv:1803.06841 [5] Barahona-Ríos, A., & Collins, T. (2023). NoiseBandNet: Controllable TimeVarying Neural Synthesis of Sound Effects Using Filterbanks. arXiv preprint arXiv:2307.08007. [6] Hagiwara, M., Cusimano, M., & Liu, J. Y. (2022). Modeling Animal Vocalizations through Synthesizers. arXiv preprint arXiv:2210.10857. [7] Andreu, S., & Aylagas, M. V. (2022, October). Neural synthesis of sound effects using flow-based deep generative models. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 18, No. 1, pp. 2-9). [8] Comunità, M., Phan, H., & Reiss, J. D. (2021). Neural synthesis of footsteps sound effects with generative adversarial networks. arXiv preprint arXiv:2110.09605 [9] Chung, Y., Lee, J., & Nam, J. (2023). Foley sound synthesis in waveform domain with diffusion model. Tech. Rep., June. [ 10] Liu, Y., & Jin, C. (2023). Conditional Sound Effects Generation with Regularized WGAN. The Applied Informatics group at Edinburgh Napier University is also looking for students to work on one of the following projects: * A new model for information literacies of community representatives * Trust, risk and digital identity for digitally-unsure citizens * Designing Meaningful Mixed Reality Experiences * Understanding Barriers and Facilitators to technology adoption among older adults: a mixed-methods approach * Playing in the Past: Investigating User Experience Design in Historical Games * Investigating digital approaches to creating meaningful experiences of place * Designing Interactive Digital Storytelling Experiences * Interpreting and interacting with archives and collections from the perspective of the creative practitioner. * Multimodal Applications for Cognitive Differences * The role of digital technologies in shifts towards sustainable behaviours – empowering end user engagement through user-centred design * The use digital tools and online information for the self-management of health * Equally Safe Sound Design * Behaviour change for Cybersecurity * Intelligent proxies to support health and social care for older adults * Playful engagement with Politics in the time of misinformation. The full description of the projects is available here<https://blogs.napier.ac.uk/scebe-research/applied-informatics/>. We welcome also innovative proposals outside the listed topics but aligned with the research interests of the Subject Group, which can be found at Applied Informatics<https://blogs.napier.ac.uk/scebe-research/applied-informatics/>. -------------- next part -------------- An HTML attachment was scrubbed... URL: <https://mail.music.vt.edu/mailman/private/sursound/attachments/20231106/c609ebc3/attachment.htm> _______________________________________________ Sursound mailing list Sursound@music.vt.edu https://mail.music.vt.edu/mailman/listinfo/sursound - unsubscribe here, edit account or options, view archives and so on.
