A manifold learning approach for personalizing HRTFS from anthropometric features

Felipe Grijalva, Luiz Martini, Dinei Florencio, Siome Goldenstein

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

38 Citas (Scopus)

Resumen

We present a new anthropometry-based method to personalize head-related transfer functions (HRTFs) using manifold learning in both azimuth and elevation angles with a single nonlinear regression model. The core element of our approach is a domain-specific nonlinear dimensionality reduction technique, denominated Isomap, over the intraconic component of HRTFs resulting from a spectral decomposition. HRTF intraconic components encode the most important cues for HRTF individualization, leaving out subject-independent cues. First, we modify the graph construction procedure of Isomap to integrate relevant prior knowledge of spatial audio into a single manifold for all subjects by exploiting the existing correlations among HRTFs across individuals, directions, and ears. Then, with the aim of preserving the multifactor nature of HRTFs (i.e. subject, direction and frequency), we train a single artificial neural network to predict low-dimensional HRTFs from anthropometric features. Finally, we reconstruct the HRTF from its estimated low-dimensional version using a neighborhood-based reconstruction approach. Our findings show that introducing prior knowledge in Isomap's manifold is a powerful way to capture the underlying factors of spatial hearing. Our experiments show, with p-values less than 0.05, that our approach outperforms using, either a PCA linear reduction, or the full HTRF, in its intermediate stages.

Idioma originalInglés
Páginas (desde-hasta)559-570
Número de páginas12
PublicaciónIEEE/ACM Transactions on Audio Speech and Language Processing
Volumen24
N.º3
DOI
EstadoPublicada - mar. 2016
Publicado de forma externa

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