Lovedee-Turner, Michael James and Murphy, Damian Thomas orcid.org/0000-0002-6676-9459 (2018) Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses. Applied Sciences. 105. ISSN 2076-3417
Abstract
Spatial impulse response analysis techniques are commonly used in the field of acoustics, as they help to characterise the interaction of sound with an enclosed environment. This paper presents a novel approach for spatial analyses of binaural impulse responses, using a binaural model fronted neural network. The proposed method uses binaural cues utilised by the human auditory system, which are mapped by the neural network to the azimuth direction of arrival classes. A cascade-correlation neural network was trained using a multi-conditional training dataset of head-related impulse responses with added noise. The neural network is tested using a set of binaural impulse responses captured using two dummy head microphones in an anechoic chamber, with a reflective boundary positioned to produce a reflection with a known direction of arrival. Results showed that the neural network was generalisable for the direct sound of the binaural room impulse responses for both dummy head microphones. However, it was found to be less accurate at predicting the direction of arrival of the reflections. The work indicates the potential of using such an algorithm for the spatial analysis of binaural impulse responses, while indicating where the method applied needs to be made more robust for more general application.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 by the authors. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number EPSRC EP/M023265/1 |
Depositing User: | Pure (York) |
Date Deposited: | 17 Jan 2018 11:50 |
Last Modified: | 07 Feb 2025 00:18 |
Published Version: | https://doi.org/10.3390/app8010105 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.3390/app8010105 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:126364 |
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