HRTF Selection by Anthropometric Regression for Improving Horizontal Localization Accuracy

Spagnol, Simone

doi:10.1109/lsp.2020.2983633

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…Measurements of 15 pinna anthropometric parameters, including 11 linear and 4 angular parameters (Table 2) were gathered for this work. There is currently no standard definition for these parameters; the parameters selected for this study are focused on obtaining a relatively integral representation of pinna features, following previous works found in the literature [8,[36][37][38]. The anthropometric parameters of each pinna were measured from the 3D models used to manufacture silicone replicas of the pinnae (Figure 3).…”

Section: Anthropometric Datamentioning

confidence: 99%

Prediction of Head Related Transfer Functions Using Machine Learning Approaches

et al. 2023

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The generation of a virtual, personal, auditory space to obtain a high-quality sound experience when using headphones is of great significance. Normally this experience is improved using personalized head-related transfer functions (HRTFs) that depend on a large degree of personal anthropometric information on pinnae. Most of the studies focus their personal auditory optimization analysis on the study of amplitude versus frequency on HRTFs, mainly in the search for significant elevation cues of frequency maps. Therefore, knowing the HRTFs of each individual is of considerable help to improve sound quality. The following work proposes a methodology to model HRTFs according to the individual structure of pinnae using multilayer perceptron and linear regression techniques. It is proposed to generate several models that allow knowing HRTFs amplitude for each frequency based on the personal anthropometric data on pinnae, the azimuth angle, and the elevation of the sound source, thus predicting frequency magnitudes. Experiments show that the prediction of new personal HRTF generates low errors, thus this model can be applied to new heads with different pinnae characteristics with high confidence. Improving the results obtained with the standard KEMAR pinna, usually used in cases where there is a lack of information.

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Section: Anthropometric Datamentioning

confidence: 99%

Prediction of Head Related Transfer Functions Using Machine Learning Approaches

et al. 2023

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“…To address this inconvenience, our objective is to use a minimal set of anthropometric features for synthesizing the HRTF phase spectra. Previous research [56] demonstrated that selecting HRTFs from a database based on the closest head width generally results in lateral perception errors within acceptable thresholds, often not exceeding 1° of localization blur. Furthermore, Algazi et al [57] established through linear regression analysis that head width is highly correlated with ITD among anthropometric features.…”

Section: A Simplifed Hrtf Selectionmentioning

confidence: 99%

PRTFNet: HRTF Individualization for Accurate Spectral Cues Using a Compact PRTF

Ko,

Lee,

Nam

et al. 2023

IEEE Access

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Spatial audio rendering relies on accurate localization perception, which requires individual head-related transfer functions (HRTFs). Previous methods based on deep neural networks (DNNs) for predicting HRTF magnitude spectra from pinna images used HRTF log-magnitude as the network output during the training stage. However, HRTFs encompass the acoustical characteristics of the head and torso, making it challenging to reconstruct the spectral cues necessary for elevation localization. To tackle this issue, we propose PRTFNet to reconstruct the individual spectral cues in HRTFs by mitigating the influence of the head and torso. PRTFNet consists of an end-to-end convolutional neural network (CNN) model and leverages a compact pinna-related transfer function (PRTF) that eliminates the impact of sound reflections from the head and torso in the head-related impulse response (HRIR) as network output. Additionally, we introduce HRTF phase personalization, a technique that utilizes the phase spectra of a selected HRTFs from a database and adjusts the phase by multiplying it by the ratio of the target listener's head width to that of the subject of the selected HRTFs. We evaluated the proposed HRTF individualization methods using the HUTUBS dataset, and the results demonstrate that PRTFNet is highly effective in reconstructing the first and second spectral cues. In terms of log spectral distortion (LSD) and effective LSD (LSDE), PRTFNet outperforms previous deep learning-based model. Furthermore, multiplying the selected phase by the head width ratio reduces the root mean square error (RMSE) of interaural time difference (ITD) by 0.003 ms.INDEX TERMS Head-related transfer functions, individualization, pinna-related transfer functions, spectral cues, spatial hearing.

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“…The ITD, on the other hand, is extracted from a HUTUBS subject and applied to the HRIRs of the generated set. The relevant HUTUBS subject is chosen using the HRTF selection algorithm proposed by Spagnol [23], where three anthropometric parameters -corresponding to head width, head depth, and shoulder circumference -are used as features of a linear regression model predicting a horizontal localization error metric. The metric is computed for all HUTUBS subjects, and the one minimizing the error is selected.…”

Section: Hrtf Set Generationmentioning

confidence: 99%

A hybrid approach to structural modeling of individualized HRTFs

Miccini

Spagnol

2021

2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)

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We present a hybrid approach to individualized head-related transfer function (HRTF) modeling which requires only 3 anthropometric measurements and an image of the pinna. A prediction algorithm based on variational autoencoders synthesizes a pinna-related response from the image, which is used to filter a measured head-andtorso response. The interaural time difference is then manipulated to match that of the HUTUBS dataset subject minimizing the predicted localization error. The results are evaluated using spectral distortion and an auditory localization model. While the latter is inconclusive regarding the efficacy of the structural model, the former metric shows promising results with encoding HRTFs.

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HRTF Selection by Anthropometric Regression for Improving Horizontal Localization Accuracy

Cited by 10 publications

References 21 publications

Prediction of Head Related Transfer Functions Using Machine Learning Approaches

Prediction of Head Related Transfer Functions Using Machine Learning Approaches

PRTFNet: HRTF Individualization for Accurate Spectral Cues Using a Compact PRTF

A hybrid approach to structural modeling of individualized HRTFs

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