Individualization of Head-Related Transfer Functions in the Median Plane using Frontal Projection Headphones

Sunder, Kaushik; Gan, Woon‐Seng

doi:10.17743/jaes.2016.0055

Cited by 5 publications

(4 citation statements)

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“…HRTFs form a separate data category. They describe the acoustic filtering of the sound by the listener's body, in particular, the head, torso, and ears [96]. The AMT stores HRTFs in the directory hrtfs as so-called SOFA files, and uses the SOFA API for MATLAB/Octave for their handling.…”

Section: Data Functions and Auxiliary Datamentioning

confidence: 99%

AMT 1.x: A toolbox for reproducible research in auditory modeling

2022

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The Auditory Modeling Toolbox (AMT) is a MATLAB/Octave toolbox for the development and application of computational auditory models with a particular focus on binaural hearing. The AMT aims for a consistent implementation of auditory models, well-structured in-code documentation, and inclusion of auditory data required to run the models. The motivation is to provide a toolbox able to reproduce the model predictions and allowing students and researchers to work with and to advance existing models. In the AMT, model implementations can be evaluated in two stages: by running so-called demonstrations, which are quick presentations of a model, and by starting so-called experiments aimed at reproducing results from the corresponding publications. Here, we describe the tools and mechanisms available within the framework of all AMT 1.x versions. The recently released AMT 1.1 includes over 60 models and is freely available as an open-source package from https://www.amtoolbox.org.

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Section: Data Functions and Auxiliary Datamentioning

confidence: 99%

AMT 1.x: A toolbox for reproducible research in auditory modeling

2022

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“…Spatial audio technologies has shown its importance in various fields, such as video conferencing, virtual reality, humanoid robot interactions and hearing aids, and there are many existing methods reproducing a spatial sound field on different devices for human listeners. To adapt those devices for great spatial experiences, it is necessary to find the most valuable acoustic cues for human hearing system [1][2][3][4]. Additionally, testing and evaluating the quality of reproduced sound field and the hearing experience of those devices is another challenging problem [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Individualized Interaural Feature Learning and Personalized Binaural Localization Model

Talagala

Zhang

et al. 2019

Applied Sciences

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The increasing importance of spatial audio technologies has demonstrated the need and importance of correctly adapting to the individual characteristics of the human auditory system, and illustrates the crucial need for humanoid localization systems for testing these technologies. To this end, this paper introduces a novel feature analysis and selection approach for binaural localization and builds a probabilistic localization mapping model, especially useful for the vertical dimension localization. The approach uses the mutual information as a metric to evaluate the most significant frequencies of the interaural phase difference and interaural level difference. Then, by using the random forest algorithm and embedding the mutual information as a feature selection criteria, the feature selection procedures are encoded with the training of the localization mapping. The trained mapping model is capable of using interaural features more efficiently, and, because of the multiple-tree-based model structure, the localization model shows robust performance to noise and interference. By integrating the direct path relative transfer function estimation, we propose to devise a novel localization approach that has improved performance in the presence of noise and reverberation. The proposed mapping model is compared with the state-of-the-art manifold learning procedure in different acoustical configurations, and a more accurate and robust output can be observed.

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“…Pursuing a different direction, several authors have proposed perceptual-based methods, where subjects choose their HRTFs through listening tests by tuning some parameters until they achieve an acceptable spatial accuracy [157,158]. Moreover, Sunder et al proposed an individualization method in the horizontal plane that uses a frontal projection headphone to introduce idiosyncratic pinna cues [159].…”

Section: Introductionmentioning

confidence: 99%

Manifold learning for spatial audio rendering

Arevalo¹

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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.

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Individualization of Head-Related Transfer Functions in the Median Plane using Frontal Projection Headphones

Cited by 5 publications

References 0 publications

AMT 1.x: A toolbox for reproducible research in auditory modeling

AMT 1.x: A toolbox for reproducible research in auditory modeling

Individualized Interaural Feature Learning and Personalized Binaural Localization Model

Manifold learning for spatial audio rendering

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