Precise Near-to-Head Acoustics with Binaural Synthesis

For enhanced immersion into a virtual scene more than just the visual sense should be addressed by a Virtual Reality system. Additional auditory stimulation appears to have much potential, as it realizes a multisensory system. This is especially useful when the user does not have to wear any additional hardware, e.g., headphones. Creating a virtual sound scene with spatially distributed sources requires a technique for adding spatial cues to audio signals and an appropriate reproduction. In this paper we present a real-time audio rendering system that combines dynamic crosstalk cancellation and multi-track binaural synthesis for virtual acoustical imaging. This provides the possibility of simulating spatially distributed sources and, in addition to that, near-to-head sources for a freely moving listener in room-mounted virtual environments without using any headphones. A special focus will be put on near-to-head acoustics, and requirements in respect of the head-related transfer function databases are discussed.

Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial

Artificial neural networks are based on computational units that resemble basic information processing properties of biological neurons in an abstract and simplified manner. Generally, these formal neurons model an input-output behaviour as it is also often used to characterize biological neurons. The neuron is treated as a black box; spatial extension and temporal dynamics present in biological neurons are most often neglected. Even though artificial neurons are simplified, they can show a variety of input-output relations, depending on the transfer functions they apply. This unit on transfer functions provides an overview of different transfer functions and offers a simulation that visualizes the input-output behaviour of an artificial neuron depending on the specific combination of transfer functions.