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

The Impact of an Accurate Vertical Localization with HRTFs on Short Explorations of Immersive Virtual Reality Scenarios

Achieving a full 3D auditory experience with head-related transfer functions (HRTFs) is still one of the main challenges of spatial audio rendering. HRTFs capture the listener's acoustic effects and personal perception, allowing immersion in virtual reality (VR) applications. This paper aims to investigate the connection between listener sensitivity in vertical localization cues and experienced presence, spatial audio quality, and attention. Two VR experiments with head-mounted display (HMD) and animated visual avatar are proposed: (i) a screening test aiming to evaluate the participants' localization performance with HRTFs for a non-visible spatialized audio source, and (ii) a 2 minute free exploration of a VR scene with five audiovisual sources in a both non-spatialized (2D stereo panning) and spatialized (free-field HRTF rendering) listening conditions. The screening test allows a distinction between good and bad localizers. The second one shows that no biases are introduced in the quality of the experience (QoE) due to different audio rendering methods; more interestingly, good localizers perceive a lower audio latency and they are less involved in the visual aspects

Perceptually Driven Interactive Sound Propagation for Virtual Environments

Sound simulation and rendering can significantly augment a user‘s sense of presence in virtual environments. Many techniques for sound propagation have been proposed that predict the behavior of sound as it interacts with the environment and is received by the user. At a broad level, the propagation algorithms can be classified into reverberation filters, geometric methods, and wave-based methods. In practice, heuristic methods based on reverberation filters are simple to implement and have a low computational overhead, while wave-based algorithms are limited to static scenes and involve extensive precomputation. However, relatively little work has been done on the psychoacoustic characterization of different propagation algorithms, and evaluating the relationship between scientific accuracy and perceptual benefits.In this dissertation, we present perceptual evaluations of sound propagation methods and their ability to model complex acoustic effects for virtual environments. Our results indicate that scientifically accurate methods for reverberation and diffraction do result in increased perceptual differentiation. Based on these evaluations, we present two novel hybrid sound propagation methods that combine the accuracy of wave-based methods with the speed of geometric methods for interactive sound propagation in dynamic scenes.Our first algorithm couples modal sound synthesis with geometric sound propagation using wave-based sound radiation to perform mode-aware sound propagation. We introduce diffraction kernels of rigid objects,which encapsulate the sound diffraction behaviors of individual objects in the free space and are then used to simulate plausible diffraction effects using an interactive path tracing algorithm. Finally, we present a novel perceptual driven metric that can be used to accelerate the computation of late reverberation to enable plausible simulation of reverberation with a low runtime overhead. We highlight the benefits of our novel propagation algorithms in different scenarios.Doctor of Philosoph

頭部伝達関数の空間領域特性モデリング

Tohoku University鈴木陽一課

Towards predicting immersion in surround sound music reproduction from sound field features

When evaluating surround sound loudspeaker reproduction, perceptual effects are commonly analyzed in relationship to different loudspeaker configurations. The presented work contributes to this by modeling perceptual effects based on acoustic properties of various reproduction formats. A model of immersion in music listening is derived from the results of an experimental study analyzing the psychological construct of immersive music experience. The proposed approach is evaluated with respect to the relationship between immersion ratings and sound field features obtained from re-recordings of the stimuli using a spherical microphone array at the listening position. Spatial sound field parameters such as inter-aural cross-correlation (IACC), diffuseness and directivity are found to be of particular relevance. Further, immersion is observed to reach a point of saturation with greater numbers of loudspeakers, which is confirmed to be predictable from the physical properties of the sound field. Although effects related to participants and musical pieces outweigh the impact of sound field features, the proposed approach is found to be suitable for predicting population-average ratings, i.e. immersion experienced by an average listener for unknown content. The proposed method could complement existing research on multichannel loudspeaker reproduction by establishing a more generalizable framework independent of particular speaker setups

Sonic Interactions in Virtual Environments

This open access book tackles the design of 3D spatial interactions in an audio-centered and audio-first perspective, providing the fundamental notions related to the creation and evaluation of immersive sonic experiences. The key elements that enhance the sensation of place in a virtual environment (VE) are: Immersive audio: the computational aspects of the acoustical-space properties of Virutal Reality (VR) technologies Sonic interaction: the human-computer interplay through auditory feedback in VE VR systems: naturally support multimodal integration, impacting different application domains Sonic Interactions in Virtual Environments will feature state-of-the-art research on real-time auralization, sonic interaction design in VR, quality of the experience in multimodal scenarios, and applications. Contributors and editors include interdisciplinary experts from the fields of computer science, engineering, acoustics, psychology, design, humanities, and beyond. Their mission is to shape an emerging new field of study at the intersection of sonic interaction design and immersive media, embracing an archipelago of existing research spread in different audio communities and to increase among the VR communities, researchers, and practitioners, the awareness of the importance of sonic elements when designing immersive environments

HRTF individualization using deep learning

The research presented in this paper focuses on Head-Related Transfer Function (HRTF) individualization using deep learning techniques. HRTF individualization is paramount for accurate binaural rendering, which is used in XR technologies, tools for the visually impaired, and many other applications. The rising availability of public HRTF data currently allows experimentation with different input data formats and various computational models. Accordingly, three research directions are investigated here: (1) extraction of predictors from user data; (2) unsupervised learning of HRTFs based on autoencoder networks; and (3) synthesis of HRTFs from anthropometric data using deep multilayer perceptrons and principal component analysis. While none of the aforementioned investigations has shown outstanding results to date, the knowledge acquired throughout the development and troubleshooting phases highlights areas of improvement which are expected to pave the way to more accurate models for HRTF individualization

High Frequency Reproduction in Binaural Ambisonic Rendering

Humans can localise sounds in all directions using three main auditory cues: the differences in time and level between signals arriving at the left and right eardrums (interaural time difference and interaural level difference, respectively), and the spectral characteristics of the signals due to reflections and diffractions off the body and ears. These auditory cues can be recorded for a position in space using the head-related transfer function (HRTF), and binaural synthesis at this position can then be achieved through convolution of a sound signal with the measured HRTF. However, reproducing soundfields with multiple sources, or at multiple locations, requires a highly dense set of HRTFs. Ambisonics is a spatial audio technology that decomposes a soundfield into a weighted set of directional functions, which can be utilised binaurally in order to spatialise audio at any direction using far fewer HRTFs. A limitation of low-order Ambisonic rendering is poor high frequency reproduction, which reduces the accuracy of the resulting binaural synthesis. This thesis presents novel HRTF pre-processing techniques, such that when using the augmented HRTFs in the binaural Ambisonic rendering stage, the high frequency reproduction is a closer approximation of direct HRTF rendering. These techniques include Ambisonic Diffuse-Field Equalisation, to improve spectral reproduction over all directions; Ambisonic Directional Bias Equalisation, to further improve spectral reproduction toward a specific direction; and Ambisonic Interaural Level Difference Optimisation, to improve lateralisation and interaural level difference reproduction. Evaluation of the presented techniques compares binaural Ambisonic rendering to direct HRTF rendering numerically, using perceptually motivated spectral difference calculations, auditory cue estimations and localisation prediction models, and perceptually, using listening tests assessing similarity and plausibility. Results conclude that the individual pre-processing techniques produce modest improvements to the high frequency reproduction of binaural Ambisonic rendering, and that using multiple pre-processing techniques can produce cumulative, and statistically significant, improvements

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