Personalized HRTF Modeling Based on Deep Neural Network Using Anthropometric Measurements and Images of the Ear

• Published in: Applied sciences Vol. 8; no. 11; p. 2180
• Main Authors: Lee, Geon, Kim, Hong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2018
• Subjects: anthropometric measurement; Anthropometry; Artificial intelligence; audio rendering; convolutional neural network; Deep learning; deep neural network; Ear; ear image; Ears & hearing; Evaluation; Fault diagnosis; head-related transfer function; International conferences; Localization; Methods; Neural networks; Pattern recognition; personalization; R&D; Research & development; Root-mean-square errors; Sound localization; Sound sources; Torso; Transfer functions; United States > US; Italy
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app8112180

This paper proposes a personalized head-related transfer function (HRTF) estimation method based on deep neural networks by using anthropometric measurements and ear images. The proposed method consists of three sub-networks for representing personalized features and estimating the HRTF. As input features for neural networks, the anthropometric measurements regarding the head and torso are used for a feedforward deep neural network (DNN), and the ear images are used for a convolutional neural network (CNN). After that, the outputs of these two sub-networks are merged into another DNN for estimation of the personalized HRTF. To evaluate the performance of the proposed method, objective and subjective evaluations are conducted. For the objective evaluation, the root mean square error (RMSE) and the log spectral distance (LSD) between the reference HRTF and the estimated one are measured. Consequently, the proposed method provides the RMSE of −18.40 dB and LSD of 4.47 dB, which are lower by 0.02 dB and higher by 0.85 dB than the DNN-based method using anthropometric data without pinna measurements, respectively. Next, a sound localization test is performed for the subjective evaluation. As a result, it is shown that the proposed method can localize sound sources with higher accuracy of around 11% and 6% than the average HRTF method and DNN-based method, respectively. In addition, the reductions of the front/back confusion rate by 12.5% and 2.5% are achieved by the proposed method, compared to the average HRTF method and DNN-based method, respectively.