Biological Aspects of Perceptual Space Formation

Pecka, Michael; Leibold, Christian; Grothe, Benedikt

doi:10.1007/978-3-030-00386-9_6

Cited by 3 publications

(9 citation statements)

References 110 publications

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“…6) indicate that this assumption is inadequate. The reasons behind the existence of long-term priors in spa-tial hearing can only be speculated at this point and may be manifold: it could reflect the spatial distribution of sound sources during everyday exposure [49], might have higher relevance from an evolutionary point of view [19], or could be related to the center of gaze as observed in barn owls [50].…”

Section: Discussionmentioning

confidence: 99%

“…Nonetheless, at this point we can only speculate about the reasons behind the existence of such a long-term prior in spatial hearing. It potentially reflects the spatial distribution of sound sources during everyday exposure [53], or it may stem from an evolutionary emphasis on high relevance auditory signals [4], or could be related to the center of gaze as observed in barn owls [54] although the processing underlying the spatial inference mechanism might be different in mammals [3]. While the model currently only considers the static scenario, it sets the foundations for future work on predicting sound localization behavior in realistic environments.…”

Section: Discussionmentioning

confidence: 99%

“…We follow [21] in that we represent the spatial information for lateral and polar dimensions provided by a single sound source via the binaural stimulus as a vector of spatial features: where x itd denotes a scalar ITD feature, x ild a scalar ILD feature, and a vector that concatenates monaural spectral features for left ear, x L,mon , and right ear, x R,mon . Each feature is assumed to be extracted by different neural pathways responsible to deliver meaningful spatial information to higher levels of the auditory system [7, 19].…”

Section: Methodsmentioning

confidence: 99%

“…Many sound-localization models assume that the auditory system estimates the sound direction by comparing observed acoustic features with their association to a direction that has been learned over time [12, 17]. This process is usually described as the template selection and relies on the hypothesis that the auditory system has access to the learned knowledge, i.e., internal templates, in order to decode the latent information from the sensory evidence and to mitigate sensory uncertainty introduced by the neural system [18, 19].…”

Section: Introductionmentioning

confidence: 99%

“…When localizing a sound source, human listeners have to deal with numerous sources of uncertainty [1]. Uncertainties originate from ambiguities in the acoustic signal encoding the source position [2] as well as the limited precision of the auditory system in decoding the received acoustic information [3,4]. Bayesian inference describes a statistically optimal solution to deal with such uncertainties in the process of perceptual decision making [5] and has been applied to model sound localization in various ways [6][7][8][9].…”

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confidence: 99%

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A Bayesian model for human directional localization of broadband static sound sources

Barumerli

Majdak

Geronazzo

et al. 2022

Preprint

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Human listeners estimate the spatial direction of a sound source from multiple auditory features and prior information on the sound direction. In this work, we describe a model of directional localization of a broadband and stationary sound source presented in an anechoic environment to a static listener. The model is based on Bayesian inference and it infers the sound direction from the spatial percept built by weighting the sensory evidence with prior beliefs. With the imposed restrictions, the sensory evidence is composed by a simplistic extraction mechanisms for two binaural features evaluating interaural disparities in time and intensity with a more detailed extraction mechanisms of monaural spectral-shape features. Our analysis considered two model variants for monaural features: amplitude spectra and their spectral gradient profiles. In addition, the model accounts for two types of uncertainties: the sensory noise in spatial cues and the response noise of pointing towards the direction. The proposed model variants were fitted to individual performance of human listeners localizing noise bursts. We then tested the predictive power of those models on the effects of localizing with other ears and sounds with rippled spectrum. This evaluation, especially with the deviation from a flat source spectrum, showed a clear preference for the model variant with spectral gradient cues, also in comparison to previously proposed localization models. Hence, the proposed model provides a tool for the evaluation of head-related transfer functions and a solid basis for future extensions towards modeling dynamic listening conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Bayesian model for human directional localization of broadband static sound sources

Barumerli

Majdak

Geronazzo

et al. 2022

Preprint

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Formation of Three-Dimensional Auditory Space

Majdak

Baumgartner

Jenny

2020

Modern Acoustics and Signal Processing

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A Bayesian model for human directional localization of broadband static sound sources

et al. 2023

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Humans estimate sound-source directions by combining prior beliefs with sensory evidence. Prior beliefs represent statistical knowledge about the environment, and the sensory evidence consists of auditory features such as interaural disparities and monaural spectral shapes. Models of directional sound localization often impose constraints on the contribution of these features to either the horizontal or vertical dimension. Instead, we propose a Bayesian model that flexibly incorporates each feature according to its spatial precision and integrates prior beliefs in the inference process. The model estimates the direction of a single, broadband, stationary sound source presented to a static human listener in an anechoic environment. We simplified interaural features to be broadband and compared two model variants, each considering a different type of monaural spectral features: magnitude profiles and gradient profiles. Both model variants were fitted to the baseline performance of five listeners and evaluated on the effects of localizing with non-individual head-related transfer functions (HRTFs) and sounds with rippled spectrum. We found that the variant equipped with spectral gradient profiles outperformed other localization models. The proposed model appears particularly useful for the evaluation of HRTFs and may serve as a basis for future extensions towards modeling dynamic listening conditions.

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Biological Aspects of Perceptual Space Formation

Cited by 3 publications

References 110 publications

A Bayesian model for human directional localization of broadband static sound sources

A Bayesian model for human directional localization of broadband static sound sources

Formation of Three-Dimensional Auditory Space

A Bayesian model for human directional localization of broadband static sound sources

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