Audio Metaphor (AuMe) is a research project aimed at designing new methodologies and tools for sound design and composition practices in film, games, and sound art. Through this project, we have identified the processes involved in working with audio recordings in creative environments, addressing these in our research by implementing computational systems that can assist human operations.
We have successfully developed Audio Metaphor for the retrieval of audio file recommendations from natural language texts, and even used phrases generated automatically from Twitter to sonify the current state of Web 2.0. Another significant achievement of the project has been in the segmentation and classification of environmental audio with composition-specific categories, which were then applied in a generative system approach. This allows users to generate sound design simply by entering textual prompts.
As we direct Audio Metaphor further toward perception and cognition, we will continue to contribute to the music information retrieval field through environmental audio classification and segmentation. The project will continue to be instrumental in the design and implementation of new tools for sound designers and artists.
See more information on the website audiometaphor.ca.
Thorogood, Miles, Jianyu Fan and Philippe Pasquier. “Soundscape Audio Signal Classification and Segmentation Using Listeners Perception of Background and Foreground Sound”. Journal of the Audio Engineering Society. Special Issue (Intelligent Audio Processing, Semantics, and Interaction), Oct 2016.
Fan, Jianyu, Miles Thorogood, and Philippe Pasquier. “Automatic Soundscape Affect Recognition Using A Dimensional Approach”. Journal of the Audio Engineering Society. Special Issue (Intelligent Audio Processing, Semantics, and Interaction), Oct 2016.
Thorogood, M., Fan, J., Pasquier, P. BF-Classifier: Background/Foreground Classification and Segmentation of Soundscape Recordings. In Proceedings of the 10th Audio Mostly Conference, Greece, 2015.
Fan, J., Thorogood, M., Riecke, B., Pasquier, P. Automatic Recognition of Eventfulness and Pleasantness of Soundscape. In Proceedings of the 10th Audio Mostly Conference, Greece, 2015.
Eigenfeldt, A., Thorogood, M., Bizzocchi, J., Pasquier, P. MediaScape: Towards a Video, Music, and Sound Metacreation. Journal of Science and Technology of the Arts 6, 2014.
Thorogood, M, Pasquier, P., and Eigenfeldt, A. (2012). “Audio Metaphor: Audio Information Retrieval for Soundscape Composition” Sound and Music Computing (SMC). Copenhagen, Denmark.
Thorogood, M., Pasquier, P. (2013). “Computationally Generated Soundscapes with Audio Metaphor” In Proceedings of the 4th International Conference on Computational Creativity (ICCC). Sydney, Australia.
Thorogood, M., Pasquier, P. (2013). “Impress: A Machine Learning Approach to Soundscape Affect Classification for a Music Performance Environment” Proceedings of the 13th International Conference on New Interfaces for Musical Expression (NIME). Daejeon + Seoul, Korea Republic .
Bizzochi, Jim, Arne Eigenfeldt, Miles Thorogood and Justine Bizzochi. “Generating Affect: Applying Valence and Arousal values to a unified video, music, and sound generation system”. Generative Art Conference. 2015. 308 – 318
Bizzochi, Jim, Arne Eigenfeldt, Philippe Pasquier and Miles Thorogood. “Seasons II: a case study in Ambient Video, Generative Art, and Audiovisual Experience”. Electronic Literature Organization Conference. British Columbia, Canada. Jun, 2016. Electronic Literature Organization.