A Model Of Head Related Transfer Functions Based On A State Space Analysis

A Model of Head-Related Transfer Functions Based on a State-Space Analysis

Head-Related Transfer Function and Virtual Auditory Display

This book systematically details the basic principles and applications of head-related transfer function (HRTF) and virtual auditory display (VAD), and reviews the latest developments in the field, especially those from the author’s own state-of-the-art research group. Head-Related Transfer Function and Virtual Auditory Display covers binaural hearing and the basic principles, experimental measurements, computation, physical characteristics analyses, filter design, and customization of HRTFs. It also details the principles and applications of VADs, including headphone and loudspeaker-based binaural reproduction, virtual reproduction of stereophonic and multi-channel surround sound, binaural room simulation, rendering systems for dynamic and real-time virtual auditory environments, psychoacoustic evaluation and validation of VADs, and a variety of applications of VADs. This guide provides all the necessary knowledge and latest results for researchers, graduate students, and engineers who work in the field of HRTF and VAD.

Modern Methodology and Applications in Spatial-Temporal Modeling

​ This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.