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Bayesian Optimization of Deep Learning Techniques for Synthesis of Head-Related Transfer Functions

Head-related transfer functions (HRTF) are used for creating the perception of a virtual sound source at horizontal angle ø and vertical angle ?. Publicly available databases use a subset of a full-grid of angular directions due to time and complexity to acquire and deconvolve responses. In this paper we build up on our prior research [5] by extending the technique to HRTF synthesis, using the IRCAM dataset, while reducing the computational complexity of the autoencoder (AE)+fully-connected-neural-network (FCNN) architecture by ˜ 60% using Bayesian optimization. We also present listening test results, demonstrating the performance of the presented approach, from a pilot study that was designed for assessing the directional cues of the proposed architecture.

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