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Deep Neural Network Based HRTF Personalization Using Anthropometric Measurements

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A head-related transfer function (HRTF) is a very simple and powerful tool for producing spatial sound by filtering monaural sound. It represents the effects of the head, body, and pinna as well as the pathway from a given source position to a listener’s ears. Unfortunately, while the characteristics of HRTF differ slightly from person to person, it is usual to use the HRIR that is averaged over all the subjects. In addition, it is difficult to measure individual HRTFs for all horizontal and vertical directions. Thus, this paper proposes a deep neural network (DNN)-based HRTF personalization method using anthropometric measurements. To this end, the CIPIC HRTF database, which is a public domain database of HRTF measurements, is analyzed to generate a DNN model for HRTF personalization. The input features for the DNN are taken as the anthropometric measurements, including the head, torso, and pinna information. Additionally, the output labels are taken as the head-related impulse response (HRIR) samples of a left ear. The performance of the proposed method is evaluated by computing the root-mean-square error (RMSE) and log-spectral distortion (LSD) between the referenced HRIR and the estimated one by the proposed method. Consequently, it is shown that the RMSE and LSD for the estimated HRIR are smaller than those of the HRIR averaged over all the subjects from the CIPIC HRTF database. 

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