Automatic speech-to-background ratio selection to maintain speech intelligibility in broadcasts using an objective intelligibility metric

Tang, Y, Fazenda, BM and Cox, TJ ORCID: 0000-0002-4075-7564 2017, 'Automatic speech-to-background ratio selection to maintain speech intelligibility in broadcasts using an objective intelligibility metric' , Applied Sciences, 8 (1) , p. 59.

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution 4.0.

Download (396kB) | Preview
[img] PDF - Accepted Version
Restricted to Repository staff only

Download (367kB) | Request a copy

Abstract

While mixing, sound producers and audio professionals empirically set the speech-to-background ratio (SBR) based on rules of thumb and their own perception of sounds. There is no guarantee that the speech content will be intelligible for the general population consuming content over a wide variety of devices, however. In this study, an approach to automatically determine the appropriate SBR for a scene using an objective intelligibility metric is introduced. The model-estimated SBR needed for a preset minimum intelligibility level was compared to the listener-preferred SBR for a range of background sounds. It was found that an extra gain added to the model estimation is needed even for listeners with normal hearing. This gain is needed so an audio scene can be auditioned with comfort and without compromising the sound effects contributed by the background. When the background introduces little informational masking, the extra gain holds almost constant across the various background sounds. However, a larger gain is required for a background that induces informational masking, such as competing speech. The results from a final subjective rating study show that the model-estimated SBR with the additional gain, yields the same listening experience as the SBR preferred by listeners.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Applied Sciences
Publisher: MDPI
ISSN: 2076-3417
Related URLs:
Funders: Engineering and Physical Sciences Research Council (EPSRC)
Depositing User: A Johnson
Date Deposited: 03 Jan 2018 16:04
Last Modified: 20 Jun 2018 20:49
URI: http://usir.salford.ac.uk/id/eprint/44889

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year