McKenzie, Thomas, Armstrong, Calum, Ward, Lauren et al. (2 more authors) (2022) Predicting the Colouration between Binaural Signals. Applied Sciences (Switzerland). 2441. ISSN 2076-3417
Abstract
Although the difference between the fast Fourier transforms of two audio signals is often used as a basic measure of predicting perceived colouration, these signal measures do not provide information on how relevant the results are from a perceptual point of view. This paper presents a perceptually motivated loudness calculation for predicting the colouration between binaural signals which incorporates equal loudness frequency contouring, relative subjective loudness weighting, cochlea frequency modelling, and an iterative normalisation of input signals. The validation compares the presented model to three other colouration calculations in two ways: using test signals designed to evaluate specific elements of the model, and against the results of a listening test on degraded binaural audio signals. Results demonstrate the presented model is appropriate for predicting the colouration between binaural signals.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Funding Information: Funding: This research was supported by a Google Faculty Research Award. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
Keywords: | Binaural audio,Colouration,Immersive audio,Signal difference,Spectral difference |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) The University of York > Faculty of Arts and Humanities (York) > Theatre, Film, TV and Interactive Media (York) |
Depositing User: | Pure (York) |
Date Deposited: | 22 Mar 2022 16:10 |
Last Modified: | 19 Nov 2024 00:42 |
Published Version: | https://doi.org/10.3390/app12052441 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.3390/app12052441 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185053 |