Archives of Acoustics, 42, 3, pp. 365–373, 2017
10.1515/aoa-2017-0038

Head-Related Transfer Function Selection Using Neural Networks

Shu-Nung YAO
http://web.ntpu.edu.tw/~snyao
National Taipei University
Taiwan, Province of China

Tim COLLINS
http://www.eee.bham.ac.uk/collinst/
Manchester Metropolitan University
United Kingdom

Chaoyun LIANG
National Taiwan University
Taiwan, Province of China

In binaural audio systems, for an optimal virtual acoustic space a set of head-related transfer functions (HRTFs) should be used that closely matches the listener’s ones. This study aims to select the most appropriate HRTF dataset from a large database for users without the need for extensive listening
tests. Currently, there is no way to reliably reduce the number of datasets to a smaller, more manageable number without risking discarding potentially good matches. A neural network that estimates the appropriateness of HRTF datasets based on input vectors of anthropometric measurements is proposed. The shapes and sizes of listeners’ heads and pinnas were measured using digital photography; the measured anthropometric parameters form the feature vectors used by the neural network. A graphical user interface (GUI) was developed for participants to listen to music transformed using different HRTFs and
to evaluate the fitness of each HRTF dataset. The listening scores recorded were the target outputs used to train the neural networks. The aim was to learn a mapping between anthropometric parameters and listener’s perception scores. Experimental validations were performed on 30 subjects. It is demonstrated that the proposed system produces a much more reliable HRTF selection than previously used methods.
Keywords: head-related transfer function; neural networks; localization; music; audio; anthropometry; pinna
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DOI: 10.1515/aoa-2017-0038

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