The increase in and demand for immersive audio content production and consumption, particularly in VR, is driving the need for tools to facilitate creation. Immersive productions place additional demands on sound design teams, specifically around the increased complexity of scenes, increased number of sound producing objects, and the need to spatialise sound in 360?. This paper presents an initial feasibility study for a methodology utilising visual object detection in order to detect, track, and match content for sound generating objects, in this case based on a simple 2D visual scene. Results show that while successful for a single moving object there are limitations within the current computer vision system used which causes complications for scenes with multiple objects. Results also show that the recommendation of candidate sound effect files is heavily dependent on the accuracy of the visual object detection system and the labelling of the audio repository used.

Authors: Turner, Daniel; Pike, Chris; Murphy, Damian
Affiliations: University of York; BBC R&D; University of York(See document for exact affiliation information.)
AES Convention: 148 (May 2020) Paper Number: 10375
Publication Date: May 28, 2020 Import into BibTeX
Subject: Applications
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=20792

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