HRTF Magnitude Synthesis via Sparse Representation of Anthropometric Features - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

HRTF Magnitude Synthesis via Sparse Representation of Anthropometric Features


We propose a method for the synthesis of the magnitudes of Head-related Transfer Functions (HRTFs) using a sparse representation of anthropometric features. Our approach treats the HRTF synthesis problem as finding a sparse representation of the subject's anthropometric features w.r.t. the anthropometric features in the training set. The fundamental assumption is that the magnitudes of a given HRTF set can be described by the same sparse combination as the anthropometric data. Thus, we learn a sparse vector that represents the subject's anthropometric features as a linear superposition of the anthropometric features of a small subset of subjects from the training data. Then, we apply the same sparse vector directly on the HRTF tensor data. For evaluation purpose we use a new dataset, containing both anthropometric features and HRTFs. We compare the proposed sparse representation based approach with ridge regression and with the data of a manikin (which was designed based on average anthropometric data), and we simulate the best and the worst possible classifiers to select one of the HRTFs from the dataset. For instrumental evaluation we use log-spectral distortion. Experiments show that our sparse representation outperforms all other evaluated techniques, and that the synthesized HRTFs are almost as good as the best possible HRTF classifier.
Fichier principal
Vignette du fichier
Piotr Bilinski - ICASSP 2014 - HRTF Synthesis.pdf (318.83 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01097303 , version 1 (19-12-2014)





Piotr Bilinski, Jens Ahrens, Mark R. P. Thomas, Ivan J. Tashev, John C. Platt. HRTF Magnitude Synthesis via Sparse Representation of Anthropometric Features. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014, Florence, Italy. pp.4468 - 4472, ⟨10.1109/ICASSP.2014.6854447⟩. ⟨hal-01097303⟩


225 View
912 Download



Gmail Facebook Twitter LinkedIn More