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- W4312385087 abstract "Several prior studies have used machine-learning techniques, such as Principal Component Analysis (PCA), to investigate the information content in unidirectional Head-Related Transfer Funcions (HRTFs). This paper builds upon this prior work to investigate the information content in full-sphere, free-field HRTFs. An artificial neural network is trained to reproduce the PCA coefficients of an HRTF from the combination of a direction vector and latent space vector identifying the HRTF. The training data comes from several different HRTF databases; the methods used to combine these databases into a single coherent dataset are described. Hyperparameter optimization techniques are then used to minimize the dimensionality of the latent space vector, providing new information about the information content required to accruately describe a full-sphere HRTF." @default.
- W4312385087 created "2023-01-04" @default.
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- W4312385087 date "2022-10-01" @default.
- W4312385087 modified "2023-09-26" @default.
- W4312385087 title "Developing a latent space to describe full-sphere head-related transfer functions" @default.
- W4312385087 doi "https://doi.org/10.1121/10.0015976" @default.
- W4312385087 hasPublicationYear "2022" @default.
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