3-D sound for virtual reality and multimedia
3-D sound for virtual reality and multimedia
Application of dimensionality reduction techniques to HRTFS for interactive virtual environments
Proceedings of the international conference on Advances in computer entertainment technology
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ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
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Rendering localized spatial audio in a virtual auditory space
IEEE Transactions on Multimedia
IEEE Spectrum
Spatial sound for video games and virtual environments utilizing real-time GPU-based convolution
Future Play '08 Proceedings of the 2008 Conference on Future Play: Research, Play, Share
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Dimensionality reduction is a statistical tool commonly used to map high-dimensional data into lower a dimensionality. The transformed data is typically more suitable for regression analysis or classification than the original data. Being of high dimensionality, HRTF data is commonly reduced using Principal Components Analysis (PCA). While highly effective at compressing data that follows the assumed model, PCA compression performance suffers when data follows a nonlinear distribution or when outliers are present. More recent data reduction techniques such as Isomap and locally linear embedding (LLE) take advantage of local neighborhood information in order to learn a more suitable basis for the HRTF data at hand. Quantitative results from previous work indicate that the embedding created by both LLE and Isomap are superior to those obtained with PCA. This paper presents a study that compares sound source localization accuracy by human observers when presented with a virtual sound synthesized using HRTFs whose dimensionality was reduced using either Isomap, LLE or PCA. Preliminary results indicate that good sound source localization judgement is obtainable using dimensionality reduced HRTFs and that Isomap and LLE produce superior results thus, confirming previous quantitative results.