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Individualized Interaural Feature Learning and Personalized Binaural Localization Model

Research School of Engineering, CECS, The Australian National University, Canberra 2601, Australia
CVSSP, University of Surrey, Guildford GU2 7JP, UK
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), entitled “Spatial feature learning for robust binaural sound source localization using a composite feature vector”.
Appl. Sci. 2019, 9(13), 2682;
Received: 15 May 2019 / Revised: 20 June 2019 / Accepted: 25 June 2019 / Published: 30 June 2019
(This article belongs to the Special Issue Mobile Spatial Audio)
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The increasing importance of spatial audio technologies has demonstrated the need and importance of correctly adapting to the individual characteristics of the human auditory system, and illustrates the crucial need for humanoid localization systems for testing these technologies. To this end, this paper introduces a novel feature analysis and selection approach for binaural localization and builds a probabilistic localization mapping model, especially useful for the vertical dimension localization. The approach uses the mutual information as a metric to evaluate the most significant frequencies of the interaural phase difference and interaural level difference. Then, by using the random forest algorithm and embedding the mutual information as a feature selection criteria, the feature selection procedures are encoded with the training of the localization mapping. The trained mapping model is capable of using interaural features more efficiently, and, because of the multiple-tree-based model structure, the localization model shows robust performance to noise and interference. By integrating the direct path relative transfer function estimation, we propose to devise a novel localization approach that has improved performance in the presence of noise and reverberation. The proposed mapping model is compared with the state-of-the-art manifold learning procedure in different acoustical configurations, and a more accurate and robust output can be observed. View Full-Text
Keywords: binaural localization; HRTF; feature learning; Spatial Hearing Model; random forest binaural localization; HRTF; feature learning; Spatial Hearing Model; random forest

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Wu, X.; Talagala, D.S.; Zhang, W.; Abhayapala, T.D. Individualized Interaural Feature Learning and Personalized Binaural Localization Model. Appl. Sci. 2019, 9, 2682.

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