Improving elevation perception with a tool for image-guided head-related transfer function selection

This paper proposes an image-guided HRTF selection procedure that exploits the relation between features of the pinna shape and HRTF notches. Using a 2D image of a subject's pinna, the procedure selects from a database the HRTF set that best fits the anthropometry of that subject. The proposed procedure is designed to be quickly applied and easy to use for a user without previous knowledge on binaural audio technologies. The entire process is evaluated by means of an auditory model for sound localization in the mid-sagittal plane available from previous literature. Using virtual subjects from a HRTF database, a virtual experiment is implemented to assess the vertical localization performance of the database subjects when they are provided with HRTF sets selected by the proposed procedure. Results report a statistically significant improvement in predictions of localization performance for selected HRTFs compared to KEMAR HRTF which is a commercial standard in many binaural audio solutions; moreover, the proposed analysis provides useful indications to refine the perceptually-motivated metrics that guides the selection

Usage of Spectral Distortion for Objective Evaluation of Personalized HRTF in the Median Plane

Measuring the head-related transfer functions (HRTFs) for each subject is a complex process. Therefore, it is necessary to develop procedures that allow the estimation of personalized HRTFs. It is common to estimate the weights of the principal component analysis (PCA) of a group of subjects based on some anthropometric parameters using multivariable regression modelling. Moreover, to objectively evaluate the goodness of fit} between the original HRTFs and the personalized ones, the spectral distortion (SD) is usually used too. However, its suitability in the median plane, in which the spectral profiles are crucial to localize a sound source, has not yet been demonstrated. This paper analyses the validity of the SD as a measure of the quality of the HRTF personalization in the median plane, from the localization point of view. The HRTFs were modelled from the weights estimated by multiple linear regression and artificial neural networks (ANNs). The SD was used to compare the HRTFs measured with those estimated. Likewise, the level of fitting accuracy of characteristic resonance and notches in the median plane was also compared. Despite the fact that the SD scores of ANNs are lower than those of the multiple linear regression and are similar to those reported by other studies, the errors obtained from analysing both central frequencies and levels for resonance and notches could be discriminated.Fil: Tommasini, Fabián Carlos. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Centro de Investigación y Transferencia en Acústica; Argentina. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Ramos, Oscar Alberto. Centro de Investigación y Transferencia en Acústica; Argentina. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Hug, Mercedes Ximena. Centro de Investigación y Transferencia en Acústica; Argentina. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bermejo, Fernando Raul. Centro de Investigación y Transferencia en Acústica; Argentina. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

Human sound localisation cues and their relation to morphology

Binaural soundfield reproduction has the potential to create realistic threedimensional sound scenes using only a pair of normal headphones. Possible applications for binaural audio abound in, for example, the music, mobile communications and games industries. A problem exists, however, in that the head-related transfer functions (HRTFs) which inform our spatial perception of sound are affected by variations in human morphology, particularly in the shape of the external ear. It has been observed that HRTFs simply based on some kind of average head shape generally result in poor elevation perception, weak externalisation and spectrally distorted sound images. Hence, HRTFs are needed which accommodate these individual differences. Direct acoustic measurement and acoustic simulations based on morphological measurements are obvious means of obtaining individualised HRTFs, but both methods suffer from high cost and practical difficulties. The lack of a viable measurement method is currently hindering the widespread adoption of binaural technologies. There have been many attempts to estimate individualised HTRFs effectively and cheaply using easily obtainable morphological descriptors, but due to an inadequate understanding of the complex acoustic effects created in particular by the external ear, success has been limited. The work presented in this thesis strengthens current understanding in several ways and provides a promising route towards improved HRTF estimation. The way HRTFs vary as a function of direction is compared with localisation acuity to help pinpoint spectral features which contribute to spatial perception. 50 subjects have been scanned using magnetic resonance imaging to capture their head and pinna morphologies, and HRTFs for the same group have been measured acoustically. To make analysis of this extensive data tractable, and so reveal the mapping between the morphological and acoustic domains, a parametric method for efficiently describing head morphology has been developed. Finally, a novel technique, referred to as morphoacoustic perturbation analysis (MPA), is described. We demonstrate how MPA allows the morphological origin of a variety of HRTF spectral features to be identified

Aprendizado de variedades para a síntese de áudio espacial

Orientadores: Luiz César Martini, Bruno Sanches MasieroTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O objetivo do áudio espacial gerado com a técnica binaural é simular uma fonte sonora em localizações espaciais arbitrarias através das Funções de Transferência Relativas à Cabeça (HRTFs) ou também chamadas de Funções de Transferência Anatômicas. As HRTFs modelam a interação entre uma fonte sonora e a antropometria de uma pessoa (e.g., cabeça, torso e orelhas). Se filtrarmos uma fonte de áudio através de um par de HRTFs (uma para cada orelha), o som virtual resultante parece originar-se de uma localização espacial específica. Inspirados em nossos resultados bem sucedidos construindo uma aplicação prática de reconhecimento facial voltada para pessoas com deficiência visual que usa uma interface de usuário baseada em áudio espacial, neste trabalho aprofundamos nossa pesquisa para abordar vários aspectos científicos do áudio espacial. Neste contexto, esta tese analisa como incorporar conhecimentos prévios do áudio espacial usando uma nova representação não-linear das HRTFs baseada no aprendizado de variedades para enfrentar vários desafios de amplo interesse na comunidade do áudio espacial, como a personalização de HRTFs, a interpolação de HRTFs e a melhoria da localização de fontes sonoras. O uso do aprendizado de variedades para áudio espacial baseia-se no pressuposto de que os dados (i.e., as HRTFs) situam-se em uma variedade de baixa dimensão. Esta suposição também tem sido de grande interesse entre pesquisadores em neurociência computacional, que argumentam que as variedades são cruciais para entender as relações não lineares subjacentes à percepção no cérebro. Para todas as nossas contribuições usando o aprendizado de variedades, a construção de uma única variedade entre os sujeitos através de um grafo Inter-sujeito (Inter-subject graph, ISG) revelou-se como uma poderosa representação das HRTFs capaz de incorporar conhecimento prévio destas e capturar seus fatores subjacentes. Além disso, a vantagem de construir uma única variedade usando o nosso ISG e o uso de informações de outros indivíduos para melhorar o desempenho geral das técnicas aqui propostas. Os resultados mostram que nossas técnicas baseadas no ISG superam outros métodos lineares e não-lineares nos desafios de áudio espacial abordados por esta teseAbstract: The objective of binaurally rendered spatial audio is to simulate a sound source in arbitrary spatial locations through the Head-Related Transfer Functions (HRTFs). HRTFs model the direction-dependent influence of ears, head, and torso on the incident sound field. When an audio source is filtered through a pair of HRTFs (one for each ear), a listener is capable of perceiving a sound as though it were reproduced at a specific location in space. Inspired by our successful results building a practical face recognition application aimed at visually impaired people that uses a spatial audio user interface, in this work we have deepened our research to address several scientific aspects of spatial audio. In this context, this thesis explores the incorporation of spatial audio prior knowledge using a novel nonlinear HRTF representation based on manifold learning, which tackles three major challenges of broad interest among the spatial audio community: HRTF personalization, HRTF interpolation, and human sound localization improvement. Exploring manifold learning for spatial audio is based on the assumption that the data (i.e. the HRTFs) lies on a low-dimensional manifold. This assumption has also been of interest among researchers in computational neuroscience, who argue that manifolds are crucial for understanding the underlying nonlinear relationships of perception in the brain. For all of our contributions using manifold learning, the construction of a single manifold across subjects through an Inter-subject Graph (ISG) has proven to lead to a powerful HRTF representation capable of incorporating prior knowledge of HRTFs and capturing the underlying factors of spatial hearing. Moreover, the use of our ISG to construct a single manifold offers the advantage of employing information from other individuals to improve the overall performance of the techniques herein proposed. The results show that our ISG-based techniques outperform other linear and nonlinear methods in tackling the spatial audio challenges addressed by this thesisDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétrica2014/14630-9FAPESPCAPE

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc