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

Majdak, Piotr; Hollomey, Clara; Baumgartner, Robert

doi:10.1051/aacus/2022011

Cited by 60 publications

(51 citation statements)

References 89 publications

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“…1) and the model comparison (function exp_osses2022 to reproduce Figs. [4][5][6][7][8][9][10][11][12][13][14][15] are publicly available as part of the AMT toolbox (https://www. amtoolbox.org) [8] as of version 1.1.0 [9].…”

Section: Data Availability Statementmentioning

confidence: 99%

A comparative study of eight human auditory models of monaural processing

et al. 2022

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A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models.

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Section: Data Availability Statementmentioning

confidence: 99%

A comparative study of eight human auditory models of monaural processing

et al. 2022

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“…In this work, HRTFs are evaluated by predicting localisation performance using the perceptual model described in Barumerli et al (2020), as implemented in the Auditory Modelling Toolbox (Majdak et al, 2022). This model simulates how a human listener would perform in a sound localisation task when provided with a HRTF different than their own.…”

Section: Evaluation Of Hrtfsmentioning

confidence: 99%

Spatial up-sampling of HRTF sets using generative adversarial networks: A pilot study

Siripornpitak

Engel

Squires

et al. 2022

Front. Signal Process.

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Headphones-based spatial audio simulations rely on Head-related Transfer Functions (HRTFs) in order to reconstruct the sound field at the entrance of the listener’s ears. A HRTF is strongly dependent on the listener’s specific anatomical structures, and it has been shown that virtual sounds recreated with someone else’s HRTF result in worse localisation accuracy, as well as altering other subjective measures such as externalisation and realism. Acoustic measurements of the filtering effects generated by ears, head and torso has proven to be one of the most reliable ways to obtain a personalised HRTF. However this requires a dedicated and expensive setup, and is time-intensive. In order to simplify the measurement setup, thereby improving the scalability of the process, we are exploring strategies to reduce the number of acoustic measurements without degrading the spatial resolution of the HRTF. Traditionally, spatial up-sampling of HRTF sets is achieved through barycentric interpolation or by employing the spherical harmonics framework. However, such methods often perform poorly when the provided HRTF data is spatially very sparse. This work investigates the use of generative adversarial networks (GANs) to tackle the up-sampling problem, offering an initial insight about the suitability of this technique. Numerical evaluations based on spectral magnitude error and perceptual model outputs are presented on single spatial dimensions, therefore considering sources positioned only in one of the three main planes: Horizontal, median, and frontal. Results suggest that traditional HRTF interpolation methods perform better than the proposed GAN-based one when the distance between measurements is smaller than 90°, but for the sparsest conditions (i.e., one measurement every 120°–180°), the proposed approach outperforms the others.

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“…Implementations of the presented models (pausch2022) and corresponding simulations (exp_pausch2022) are publicly available as part of the Auditory Modeling Toolbox (AMT, https://www. amtoolbox.org) [89] in the release of version 1.2.0 [43].…”

Section: Fundingmentioning

confidence: 99%

Hybrid multi-harmonic model for the prediction of interaural time differences in individual behind-the-ear hearing-aid-related transfer functions

2022

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Spatial sound perception in aided listeners partly relies on hearing-aid-related transfer functions (HARTFs), describing the directional acoustic paths between a sound source and the hearing-aid (HA) microphones. Compared to head-related transfer functions (HRTFs), the HARTFs of behind-the-ear HAs exhibit substantial differences in spectro-temporal characteristics and binaural cues such as interaural time differences (ITDs). Since assumptions on antipodal microphone placement on the equator of a three-concentric sphere are violated in such datasets, predicting the ITDs via Kuhn’s simple analytic harmonic model entails excessive errors. Although angular ear-canal offsets have been addressed in an extended Woodworth model, the prediction errors remain large if the frequency range does not comply with the model specifications. Tuned to the previously inaccurately modelled frequency range between 500 Hz and 1.5 kHz, we propose a hybrid multi-harmonic model to predict the ITDs in HRTFs and HARTFs for arbitrary directions in the horizontal plane with superior accuracy. The target model coefficients are derived from individual directional measurements of 30 adults, wearing two dual-microphone behind-the-ear HAs and two in-ear microphones. Model individualisation is facilitated by the availability of polynomial weights that are applied to subsets of individual anthropometric and HA features to estimate the target model coefficients. The model is published as part of the Auditory Modeling Toolbox (AMT, pausch2022) and supplemented with the individual features and directional datasets.

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

Cited by 60 publications

References 89 publications

A comparative study of eight human auditory models of monaural processing

A comparative study of eight human auditory models of monaural processing

Spatial up-sampling of HRTF sets using generative adversarial networks: A pilot study

Hybrid multi-harmonic model for the prediction of interaural time differences in individual behind-the-ear hearing-aid-related transfer functions

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