Ensemble Learning Based HRTF Personalization Using Anthropometric Features

Abstract

<p>In this paper, we propose an ensemble learning based model to synthesize the logarithmic magnitude response of head-related transfer function (HRTF) using anthropometric features. We first cluster subjects based on relevant anthropometric features to reduce differences within each group, then we use the ensemble learning algorithm on clustered results to predict the log-magnitude HRTF. In the training phase, three deep neural networks (DNNs), each of which aims to predict log-magnitude HRTFs in a particular group, are trained using anthropometric and angle-related features. Afterward, another DNN is trained to integrate estimates from the three group-wise DNNs into log-magnitude HTRFs. The proposed model is compared with a baseline DNN model and our previously proposed model, which incorporates an auto-encoder for dimensionality reduction. Experimental results show that the proposed model performs the best in synthesizing log-magnitude HRTFs in terms of the log-spectral distortion (LSD) measure with great stability.</p>

Modeling of Individual Head-Related Transfer Functions (HRTFs) Based on Spatiotemporal and Anthropometric Features Using Deep Neural Networks

“…Chun et al introduced a fully deep neural network (DNN) model for estimating personalized HRTFs based on anthropometrics. They designed a robust DNN architecture and evaluated it for a specific direction of arrival [25]. Lee and Kim expanded on this by introducing a combination of convolutional neural networks (CNN) and DNNs, using ear images for the CNN and anthropometric measurements for the feedforward DNN [26].…”