A High-Resolution Individual 3D Adult Head and Torso Model for HRTF Simulation and Validation: HRTF Measurement

Braren, Hark Simon; Fels, Janina

doi:10.18154/rwth-2020-06761

Cited by 3 publications

(2 citation statements)

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“…Reasons for this can be sourced from the assumption of a spherical geometric model [26] for calculating H E and H HA , see Figure 6 and equations (A1) and (A3). Calculations based on a refined ellipsoidal head [21] or extracting the angles based on photos from the top, or photogrammetric data [19,75,76] would result in smaller horizontal angles and likely lead to more accurate estimations in HARTF datasets using Model 2+. The observations for Model 1 and Model 2+ also apply to the results when comparing the model-based broadband ITD predictions to measurement-based narrowband ITD estimations, see Figure 10.…”

Section: Prediction Of the Arguments Of The Interauraltime-difference...mentioning

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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Section: Prediction Of the Arguments Of The Interauraltime-difference...mentioning

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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“…The reason for including a relatively large number of HRTFs (74 in total) was the need to thoroughly test the generalization property of the developed method. The employed HRTFs could be divided into thirteen groups based on their origin, e.g., the institution where the measurements were undertaken [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46] (see Table 3 3).…”

Section: Repository Of Binaural Music Recordingsmentioning

confidence: 99%

Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

Zieliński

Antoniuk

Johnson

2022

J AUDIO SPEECH MUSIC PROC.

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One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach.

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Ensemble width estimation in HRTF-convolved binaural music recordings using an auditory model and a gradient-boosted decision trees regressor

Antoniuk,

Zieliński,

Lee

2024

J AUDIO SPEECH MUSIC PROC.

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A High-Resolution Individual 3D Adult Head and Torso Model for HRTF Simulation and Validation: HRTF Measurement

Cited by 3 publications

References 0 publications

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

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

Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

Ensemble width estimation in HRTF-convolved binaural music recordings using an auditory model and a gradient-boosted decision trees regressor

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