Assessing the Impact of Head-Related Transfer Function Individualization on Task Performance: Case of a Virtual Reality Shooter Game

Poirier-Quinot, David; Katz, Brian F. G.

doi:10.17743/jaes.2020.0004

Cited by 14 publications

(13 citation statements)

References 18 publications

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“…In addition, it was characterised by the presence of goal-directed actions toward each sound. We built on works in which interactions with sound sources occurred using reaching movements, as in some pioneers VR approaches [ 21 , 22 ] and more recent studies [ 23 ]. A motor approach based on hand movements in the space of the sound sources has also been adopted by Valzolgher and colleagues [ 13 ], in a study that demonstrated the effect of kinesthetic cues when re-learning to localize sounds with one ear plugged, as here.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, adapting to new auditory cues may be easier through motor interactions with the sound sources, using tasks in which acoustic stimulation results from the subjects’ own movements [ 13 ] or in which participants are encouraged to act towards sounds [ 19 , 20 ]. For instance, participants could be asked to hit a moving sound presented in a virtual auditory space using a hand-held tool [ 21 , 22 ], or to shoot audio-visual moving targets in virtual reality [ 23 ]. Other relevant examples include the work of Steadman and colleagues, in which participants were required to actively point their head toward sounds [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Adapting to altered auditory cues: Generalization from manual reaching to head pointing

et al. 2022

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Localising sounds means having the ability to process auditory cues deriving from the interplay among sound waves, the head and the ears. When auditory cues change because of temporary or permanent hearing loss, sound localization becomes difficult and uncertain. The brain can adapt to altered auditory cues throughout life and multisensory training can promote the relearning of spatial hearing skills. Here, we study the training potentials of sound-oriented motor behaviour to test if a training based on manual actions toward sounds can learning effects that generalize to different auditory spatial tasks. We assessed spatial hearing relearning in normal hearing adults with a plugged ear by using visual virtual reality and body motion tracking. Participants performed two auditory tasks that entail explicit and implicit processing of sound position (head-pointing sound localization and audio-visual attention cueing, respectively), before and after having received a spatial training session in which they identified sound position by reaching to auditory sources nearby. Using a crossover design, the effects of the above-mentioned spatial training were compared to a control condition involving the same physical stimuli, but different task demands (i.e., a non-spatial discrimination of amplitude modulations in the sound). According to our findings, spatial hearing in one-ear plugged participants improved more after reaching to sound trainings rather than in the control condition. Training by reaching also modified head-movement behaviour during listening. Crucially, the improvements observed during training generalize also to a different sound localization task, possibly as a consequence of acquired and novel head-movement strategies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adapting to altered auditory cues: Generalization from manual reaching to head pointing

et al. 2022

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“…The premise of the present study is that HRTF selection can be reduced to its essential: that given the variance on bestmatch selection [17,18], and its limited impact on immediate localization performance [6,18], it is preferable to capitalize on training to improve localization accuracy. Still, previous studies have shown that using perceptually poorly-rated HRTFs led to a significant increase in adaptation time compared to best-rated ones [6,19].…”

Section: Previous Work 21 Hrtf Selection Methodsmentioning

confidence: 99%

On the improvement of accommodation to non-individual HRTFs via VR active learning and inclusion of a 3D room response

Poirier-Quinot

Katz

2021

Acta Acust.

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This study examines the efficiency of a training protocol using a virtual reality application designed to accelerate individual’s selection of, and accommodation to, non-individualized HRTF profiles. This training introduces three elements to hasten audio localization performance improvement: an interactive HRTF selection method, a parametric training program based on active learning, and a relatively dry room acoustic simulation designed to increase the quantity of spatial cues presented. Participants rapidly selected an HRTF (≈5 min) followed by training over three sessions of 12 min distributed over 5 days. To study the impact of the room acoustic component on localization performance evolution, participants were divided into two groups: one acting as control reference, training with only anechoic renderings, the other training in reverberant conditions. The efficiency of the training program was assessed across groups and the entire protocol was assessed through direct comparisons with results reported in previous studies. Results indicate that the proposed training program led to improved learning rates compared to that of previous studies, and that the included room response accelerated the learning process.

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“…Combining the desire for robust evaluation techniques with realistic use cases requires a delicate balance of experimental design in order to present a real-world like context while still maintaining the required laboratory controllability in order to obtain meaningful exploitable data (e.g. [12]).…”

Section: Immersionmentioning

confidence: 99%

The SONICOM Project: Artificial Intelligence-Driven Immersive Audio, From Personalization to Modeling [Applications Corner]

Picinali

Katz

Geronazzo

et al. 2022

IEEE Signal Process. Mag.

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Every individual perceives spatial audio differently due in large part to the unique and complex shape of their ears and head. Therefore, high-quality headphone-based spatial audio should be uniquely tailored to each listener in an effective and efficient manner. Artificial Intelligence (AI) is a powerful tool that can be used to drive forward research in spatial audio personalisation. The SONICOM project aims to employ a data-driven approach to link the physiological characteristics of the ear to the individual acoustic filters which allow us to localise sound sources and to perceive them as being located around us. A small amount of data acquired from users could allow personalised audio experiences, and AI could facilitate this by offering a new perspective on the matter. A Bayesian approach to computational neuroscience and binaural sound reproduction will be linked to create a metric for AI-based algorithms that will predict realistic spatial audio quality. Being able to consistently and repeatedly evaluate and quantify the improvements brought by technological advancements, as well as the impact these have on complex interactions in virtual environments, will be key for the development of new techniques and unlocking new approaches to understanding the mechanisms of human spatial hearing and communication.

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Assessing the Impact of Head-Related Transfer Function Individualization on Task Performance: Case of a Virtual Reality Shooter Game

Cited by 14 publications

References 18 publications

Adapting to altered auditory cues: Generalization from manual reaching to head pointing

Adapting to altered auditory cues: Generalization from manual reaching to head pointing

On the improvement of accommodation to non-individual HRTFs via VR active learning and inclusion of a 3D room response

The SONICOM Project: Artificial Intelligence-Driven Immersive Audio, From Personalization to Modeling [Applications Corner]

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