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Open AccessArticle

Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses

by Michael Lovedee-Turner *,†,‡ and Damian Murphy
Communication Technologies Research Group, Department of Electronic Engineering, University of York, York YO10 5DD, UK
*
Author to whom correspondence should be addressed.
Current address: Audio Lab, Department of Electronic Engineering, University of York, York YO10 5DD, UK.
Binaural model code, neural network code, and direct sound and reflection dataset will be made available at: 10.5281/zenodo.1038021.
Academic Editor: Tapio Lokki
Appl. Sci. 2018, 8(1), 105; https://doi.org/10.3390/app8010105
Received: 30 October 2017 / Revised: 12 December 2017 / Accepted: 26 December 2017 / Published: 12 January 2018
(This article belongs to the Special Issue Sound and Music Computing)
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. View Full-Text
Keywords: machine-hearing; machine-learning; binaural room impulse response; spatial analysis; direction of arrival machine-hearing; machine-learning; binaural room impulse response; spatial analysis; direction of arrival
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Lovedee-Turner, M.; Murphy, D. Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses. Appl. Sci. 2018, 8, 105.

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