Predicting the effect of headphones on the time to localize a target in an auditory-guided visual search task

Abstract

In augmented reality scenarios, headphones obstruct the direct path of the sound to the ears, affecting the users’ abilities to localize surrounding sound sources and compromising the immersive experience. Unfortunately, the assessment of the perceptual implications of wearing headphones on localization in ecologically valid scenarios is costly and time-consuming. Here, we propose a model-based tool for automatic assessment of the dynamic localization degradation (DLD) introduced by headphones describing the time required to find a target in an auditory-guided visual search task. First, we introduce the DLD score obtained for twelve headphones and the search times with actual listeners. Then, we describe the predictions of the headphone-induced DLD score obtained by an auditory model designed to simulate the listener’s search time. Our results indicate that our tool can predict the degradation score of unseen headphones. Thus, our tool can be applied to automatically assess the impact of headphones on listener experience in augmented reality applications.

1 Introduction

Auditory augmented-reality (AR) applications enhance the real world with virtual sounds, often presented via headphones (Nagele et al., 2021; Neidhardt and Zerlik, 2021; Schneiderwind et al., 2021), or more generally, via head-worn devices (HWDs) with acoustic drivers, such as head-mounted displays. These HWDs interfere with the natural transmission of the sound to the ears (Zimpfer and Sarafian, 2014; Ahrens et al., 2019; Denk et al., 2020; Lladó et al., 2022b; Poirier-Quinot and Lawless, 2023). This interference may affect the user’s ability to localize sounds from their surrounding, therefore potentially compromising the level of immersion (Gupta et al., 2018; Schneiderwind et al., 2021) or even safety (Brungart et al., 2004; Simpson et al., 2005). The development of AR-compatible headphones without perceptual degradation of the natural sound is considered one of the main technical challenges in binaural sound reproduction (Engel and Picinali, 2017; Nagele et al., 2021).

Multiple behavioral studies have shown that wearing an HWD hinders the listeners’ localization performance (Vause and Grantham, 1999; Bolia et al., 2001; Zimpfer and Sarafian, 2014; Brown et al., 2015; Lladó et al., 2022b). Listeners localize sounds by extracting the spatial information (e.g., source direction) from acoustic features generated by the interaction between the listeners’ anatomy and the acoustic field (Middlebrooks, 2015). Previous studies have shown that listeners wearing an HWD demonstrate significantly larger localization errors when compared to the open-ear condition, especially along the front-back and up-down dimensions (Vause and Grantham, 1999; Van den Bogaert et al., 2006; Van den Bogaert et al., 2008; Brungart et al., 2007; Zimpfer and Sarafian, 2014; Denk et al., 2018; Denk et al., 2019; Lladó et al., 2022b). However, those studies considered static conditions where both listener and sound source do not move. Such conditions do not necessarily represent an ecologically valid scenario for AR where listeners dynamically interact with the environment (Simpson et al., 2005; Neidhardt and Zerlik, 2021).

In order to introduce ecologically valid elements in evaluating HWDs, search tasks have been proposed (Bolia et al., 2001; Simpson et al., 2005). Within such tasks, the subject is asked to identify the position of the target and the search time required to respond is measured. While these paradigms have been extensively applied to assess visual perception (Wolfe, 1994; Eckstein, 2011; Wolfe and Horowitz, 2017), some studies included auditory cues to aid the task (Perrott et al., 1996; Bolia et al., 1999; Bolia et al., 1001; Simpson et al., 2005). The combination of auditory and visual cues result in faster responses than visual-only cues, especially when introducing distractors. Following this idea, an auditory-guided visual search test has shown that the time to find the visual source is a sensitive behavioural measure to study the effect of altered localization cues caused by HWDs (Bolia et al., 2001; Simpson et al., 2005; Lladó et al., 2024). This task appears to be of higher ecological relevance than a simple localization task under static listening conditions because it involves the integration of visual and auditory cues over time (Simpson et al., 2005).

Behavioural tests are time-consuming and require specific hardware. As a more efficient alternative, data analysis methods are employed to model the effects introduced by HWDs. One approach tries to identify the degradation of monaural cues by evaluating the spectral average ratio between the magnitudes of the transfer functions measured on the dummy head with and without the HWD (Genovese et al., 2018; Gupta et al., 2018; Porschmann et al., 2019; Denk et al., 2020; Meyer-Kahlen et al., 2020; Schneiderwind et al., 2021). Similarly, the evaluation of the interaural time and level differences obtained from the measurements with HWDs demonstrated how interaural cues are degraded compared to the measurements without the HWDs (Genovese et al., 2018; Gupta et al., 2018; Porschmann et al., 2019; Denk et al., 2020; Lladó et al., 2022a). Both evaluation approaches are limited to static scenarios and do not consider the dynamic interaction between the listener and the environment.

This article proposes a method to computationally assess the spatial effects of wearing HWDs in the auditory-guided visual search task. Our study is visualized in Figure 1, with our method shown in the lower path. As an input, our method requires the acoustically measured head-related transfer functions (HRTFs) of the HWDs under test. As the output, the method returns the degree of dynamic localization degradation on a six-level scale, with one representing the open-ear condition and six representing the most severe degradation. First, an auditory model simulates the listener’s behavior in the auditory-guided visual search task from Lladó et al. (2024) in the open-ear and HWD conditions. For this step, we took a Bayesian auditory localization model for static conditions (Barumerli et al., 2023) and extended it to account for temporal integration and voluntary head movements of a listener searching for the target (Wolfe, 1994). Then, our method classifies the model’s estimates to return the degree of degradation. To test our method, we consider twelve commercially available headphones to evaluate the predicted degradations of against those reported in a previous behavioral experiment (Lladó et al., 2024).

FIGURE 1

2 Degree of degradation: Behavioral data

We describe here the relevant elements of the behavioural experiment presented in Lladó et al. (2024) and introduce the degradation score based on a classification method.

2.1 Experimental task

The previously conducted behavioral experiment collected the search time required to find a target loudspeaker showing an even number of LEDs and playing a sound (Lladó et al., 2024). Twenty subjects between 19 and 39 years of age with self-reported normal hearing and normal or corrected vision were tested on 13 conditions. One condition served as the reference as subjects were listening with their open ears (OE), i.e., without any headphones on. In the twelve other conditions, subjects wore one of the headphones described in Table 1, covering a representative range of commercially available products applicable in AR systems.

The experiment was conducted in the anechoic room ‘Wilska’ at the Aalto Acoustics Lab, Espoo, Finland. Thirty-two Genelec 8331A coaxial loudspeakers were distributed in a spherical array with a radius of 2.04 m from the center of the room. Each loudspeaker was equipped with a 4-LED board forming a 2 × 2 matrix (15 mm × 15 mm for the LED centers) to display visual information. Two hand-held buttons were used as the interface for the listeners to control the experiment and give responses.

Each trial began with the participant facing the frontal loudspeaker. After releasing the hand-held buttons and a break of 1 s, the stimulus presentation from the target loudspeaker started. The sound stimulus was intermittent pink noise (250 ms on, 250 ms off, with onset and offset ramps of 10 ms) with an A-weighted sound pressure level of 65 dB SPL. During each trial, LEDs in all loudspeakers were illuminated but only the target loudspeaker had an even number of LEDs. The goal was to find the target and report the number of illuminated LEDs. The participants were instructed to press the left hand-held button if the number of illuminated LEDs was two, or the right one if the number of illuminated LEDs was four. The participants were instructed to respond as quickly as possible. Trials stopped immediately after a response was given. There was an upper limit of 14 noise bursts limiting each trial to 7 s, but this limit was never reached. More details on the methods can be found in Lladó et al. (2024).

2.2 Search time

The search time T_j,s,h was the time between the stimulus onset and the participant response for each trial j and subject s being tested with headphones h. The search times were then normalized by applying the z-transform:

with μ_s representing the mean and σ_s the standard deviation across all trials j of a subject s under all conditions together. The normalized search time contains information about the degree of degradation caused by each headphones while being normalized across subjects.

The left panel of Figure 2 shows the normalized search times per headphones condition. The median search time in the ‘OE’ condition was 0.96 s, and resulted in a normalized search time of −0.88. The headphones generally increased the search times and the slowest responses were found using headphones ‘K’, which resulted in a median search time of 1.68 s and a normalized search time of 0.05. For an exhaustive analysis focused on matching the acoustic characteristics of each studied headphones to the search times, see Lladó et al. (2024).

FIGURE 2

Lladó et al. (2024). Left panel: Normalized search time per condition (‘OE’: open-ear condition, i.e., without any headphone). Each point represents the median search time of a participant. Data points are colored according to the listener-dependent DLD classification; boxplots are colored according to the DLD score assigned to each headphone. Right panel: Histogram of all available normalized search times (bars), with the individual Gaussians that define the DLD scale (color lines) and the joint GMM (black line) fitted to the normalized search times.

2.3 Dynamic localization degradation (DLD) score

In order to classify the degree of degradation caused by a HWD, we introduce the dynamic localization degradation (DLD) score. The DLD score is based on the search time obtained in the behavioural experiment for each subject s and headphones h. The DLD score is an output of a classification and thus more generic and easier to interpret than the absolute search time that largely depends on specific choices in task design (Liu and Wickens, 1992).

In order to calculate the DLD score, the normalized and cleaned search times were the input to a classifier. The classifier is based on the Gaussian mixture model (GMM), which was selected because of its simplicity and efficiency (Peel and MacLahlan, 2000; McLachlan et al., 2019). To fit the GMM, we used the function fitgmdist from MATLAB version 2022a (Mathworks Inc.). The number of Gaussian components K was selected to minimize the Akaike information criterion (AIC Akaike, 1974). Before training the classifier, we excluded extremely long search times (less than 4%) by removing trials showing normalized time larger than three scaled median absolute deviations from the median (rmoutliers using the median method in MATLAB version 2022a, Mathworks Inc.). The right panel of Figure 2 shows the histogram of the normalized search times with the fitted Gaussians. The minimum AIC was reached with K = 6 Gaussians.

Then, we sorted the fitted Gaussians by their mean μ_k with the idea of the sorting order reflecting the DLD score. For each subject s and condition h, the median normalized time was computed as the median over all trials. Each Gaussian component was evaluated for . The DLD score S_s,h was computed as:where μ_k and σ_k are the mean and standard deviation, respectively, of the kth Gaussian. Table 2 shows the averages μ_k and standard deviations σ_k of the individual Gaussians after the sorting.

The color in Figure 2 (left panel) shows the computed values for the DLD scale for each headphone, both at the individual level (circles) and group level (boxplots). The algorithm assigned the ‘OE’ condition to the first cluster. Headphones ‘A’ were assigned to the second cluster. The headphones ‘F’, ‘B’, ‘C’, ‘D’, ‘E’, and ‘J’ were assigned to the third cluster. The headphones ‘G’, ‘I’, ‘H’, ‘L’, and ‘K’ were assigned to the fourth cluster. The fifth cluster accounted for the DLD of some listeners, but after computing the median over the group, these DLD were assigned to the fourth cluster. The sixth cluster was not assigned at all because it contained individual trials with especially large search times only.

3 Degree of degradation: Predictions

We predicted the degree of degradation in three steps. First, we predicted the search time by means of an auditory model. This model requires acoustic data about the headphones in the form of HRTFs. Second, we mapped the predicted search time to the normalized search times. Third, the DLD score was computed by means of the classification described in Sec. 2.3.

3.1 HRTFs dataset

For the thirteen conditions included in the experimental task, HRTFs of a head-and-torso simulator (KEMAR 45BC, G.R.A.S. Inc.) wearing the studied headphones were measured. These HRTFs were measured in the same room, with the same equipment and in the same conditions as in the experimental task. The HRTF dataset is available online (see Data Availability Statement).

3.2 Auditory model

The model simulates the active search task on a trial basis, assuming that listeners accumulate spatial information over subsequent sounds. Therefore, the auditory model implements an online estimation of the source direction as an iterative mechanism as shown in Figure 3. The model treats each noise burst as a stationary observation and extracts the directional information following the Bayesian model proposed in Barumerli et al. (2023). The model combines the information from subsequent observations utilizing Bayesian belief updating (Ma et al., 2023). Importantly, the model only incorporates visual evidence in the form of a visual check, performed after the acoustic cues are processed. This is motivated by the experimental results obtained by Simpson et al. (2005), which show that the task performance in open ears conditions does not vary significantly when increasing the number of distractors (locations with an odd number of illuminated LEDs and without a corresponding sound stimulus) from five to fifty. These results suggest that the acoustic cues are dominant over the visual ones for this specific task.

FIGURE 3

The model starts by extracting a set of noisy spatial features x_t from the binaural stimulus generated by a virtual sound source. The binaural stimulus was generated by filtering a 250 ms noise burst with the HRTF of the location of the virtual source φ relative to the head direction φ_h. From the binaural stimulus, the model computes four spatial features: interaural time difference x_itd, interaural level difference x_ild, and monaural spectral gradients for the left and right ears (Baumgartner et al., 2014; Barumerli et al., 2023). Further, the model accounts for the uncertainties of the hearing system by adding Gaussian noise δ with zero mean and covariance matrix Σ (Barumerli et al., 2023). As a result, for the iteration t, we define the spatial features x_t as:Additionally, the noise covariance matrix Σ is diagonal and characterized as:where and are the variances associated with the interaural time and level differences, and is a diagonal matrix for the monaural features with I being the identity matrix scaled by the value .

From the set of spatial features x_t, the model uses Bayesian inference to estimate the probability of the sound direction φ in each iteration t. To this end, the model weights the likelihood function with the prior distribution to obtain the posterior distribution:We simplified p (x_t|φ, x_1:t−1) to p (x_t|φ) since x_t is conditionally independent from x_1:t−1 given φ.

The computation of the likelihood function follows the model for static sound localisation (Barumerli et al., 2023) which compares x_t to the feature templates X_φ containing noiseless features of Eq. 3 for every sound direction φ. The template features were computed from the acoustically measured HRTFs and interpolated over a quasi-uniform spherical grid containing N = 1980 points generated with a quadrature of spherical t-designs (Hardin and Sloane, 1996; Gräf and Potts, 2011). Spatial interpolation was based on 15th-order spherical harmonics followed by Tikhonov regularization (Zotkin et al., 2009).

For the first noise burst in the sequence, the prior distribution is uniform p(φ) = N⁻¹, i.e., we assume that the subject has no information about the actual sound location at the beginning of the trial. Then, the posterior distribution is calculated and its maximum used to determine the model’s current estimate of sound direction. Then, the model simulates the head rotation towards with the head direction being:where m ∼vMF(0, κ_m) accounts for the uncertainty in the sensorimotor process and is defined as the von Mises-Fisher distribution with zero mean and concentration parameter κ_m (Ley and Verdebout, 2017). Importantly, the concentration parameter κ_m can be transformed to a dispersion parameter with the formula .

To update the spatial belief for the next time point t + 1, the posterior distribution p (φ|x_1:t) is propagated as the new prior distribution, which is additionally rotated in an egocentric manner (Rodrigues, 1840) to account for the new head orientation. The model iterates over further time points until the angular distance between the new head orientation and the target source , with 15° corresponding to half of the angular distance between two adjacent loudspeakers in the experiment. The computation of this angular distance emulates the visual check in the experimental task, and represents the case in which the checked loudspeaker has an even number of illuminated LEDs. If this condition is not met, then the seen loudspeaker has an odd number of illuminated LEDs, and the observer continues the search task. An example of a simulated trial with the belief update process is shown in Figure 4. Finally, the model outputs the number of noise bursts needed to find the source, which is proportional to the predicted search time .

FIGURE 4

3.2.1 Model calibration and evaluation

In order to calibrate the model, we predicted the search time for each target direction j and headphones h. Given the model stochasticity, we relied on a Monte Carlo approximation to get an average estimate of by sampling 20 times the model’s output for each target direction. The model parameters controlling the sensory and motor uncertainties were set to the medians from Barumerli et al. (2023) (i.e., σ_itd = 0.569 jnd, σ_ild = 1 dB, σ_mon = 1.25 dB and σ_m = 14°) because they demonstrated to return reasonable predictions at a group level and for stationary broadband noise bursts (Daugintis et al., 2023).

For each headphones h, we computed as the mean over all target directions j tested in the actual behavioural experiment. Then, was mapped to represent the normalized search times from the behavioral experiment by means of a linear model consisting of a main factor and an intercept:where β₁ and β₀ were fitted with the function fitlm (MATLAB version R2022a, Mathworks, Inc.).

Figure 5 shows the predicted normalized search times against the median actual normalized search times from the behavioral data . The linear model coefficients resulted in β₁ = 0.3489, β₀ = −1.8871. The model was able to find the targets in a similar ranking as the actual listeners, with the predicted search ranging from 3 to 5.76 s (compared to the actual search times ranging from 0.96 to 1.68 s). Despite these differences, the predictions showed a reasonably good linear dependency with the actual data (Pearson’s correlation coefficient of R² = 0.59).

FIGURE 5

3.3 Score predictions

We evaluated the ability and the robustness of our methodology to predict DLD score by combining the auditory model presented in the previous section and the classification method presented in Sec 2.3. Because of the limited amount of HWDs available to this study that might hinder the generalization over unseen devices, we performed a leave-one-out cross validation (Hastie et al., 2009, Chapter 7) where we compared the actual and predicted DLD scores.

We calculated the predictions for a specific pair of headphones h by excluding the corresponding behavioral data from the training procedure of the classifier. For each headphones h, the model estimates were mapped as in Eq. 7 after excluding the behavioural data for the headphones h. This resulted in computing a new linear model for each LOO evaluation, and mapping the auditory-model estimates into the normalized search times . The mean (± standard deviation) coefficients of the linear model over LOO iterations were β_1,LOO = 0.3484 ± 0.01, and β_0,LOO = −1.8838 ± 0.05. Thus, the coefficients for the LOO cross-validation were consistently similar to the coefficients obtained for the whole dataset (β₁ = 0.3489, β₀ = −1.8871).

Similarly, the cluster centers of the classifier were then recomputed from the behavioral data without data from headphones h. To avoid the number of clusters fluctuating depending on the excluded headphones, K was six as computed in Sec. 2.3. The most-right column of Table 2 shows the averages calculated from the parameters of the cluster centers. The stable values for the Gaussian parameters, μ_LOO,k and σ_LOO,k, showed that the GMM classifier did not vary significantly over LOO iterations.

Finally, we predicted the DLD score by classifying the auditory model’s simulated normalized search times with the method reported in section 2.3. The actual DLD score was computed by adopting the model parameters obtained using all the headphones (see left panel of Table 2 for GMM parameters; Section 3.2.1 for the linear model coefficients). The predicted data was computed by the model parameters obtained excluding each pair of headphones from the data following the LOO validation. Figure 6 shows the predicted DLD score for each headphones and compares them to the actual DLD score. The DLD scores were predicted correctly for eleven out of thirteen conditions. The other two conditions were missed by only one score ( vs. S_B = 3 and vs. S_E = 3). Due to the stability of the parameters for the LOO approach and the correct classification rate for the DLD scores, the mean values of β_1,LOO = 0.3484 and β_0,LOO = −1.8838 are recommended as the linear model coefficients to classify unseen HWDs. Similarly, the mean parameters for the GMM classifier μ_LOO,k and σ_LOO,k (see right panel of Table 2) are recommended for unseen HWDs.

FIGURE 6

4 Discussion

We proposed a method to automatically assess the increased search time to find a target observed when listeners wear an HWD. The assessment relies on predicting the dynamic localization degradation (DLD) score, a six-level scale based on subjects’ time to find a target in the auditory-guided visual search task. Furthermore, we proposed an auditory model to predict the DLD score of an HWD. We demonstrated the robustness of the DLD predictions by means of a cross-validation approach to account for unseen HWDs. Our method has the advantage of being more ecologically valid as compared to contrasting the effects of an HWD by means of localization performance obtained in static tasks (Simpson et al., 2005).

4.1 Actual and predicted degree of degradation

Our classification results indicate that our clustering procedure is sensitive to classifying headphones by means of normalized search times. We found that even a slight deviation from the ‘OE’ condition can increase search time. For example, ‘OE’ and the headphones ‘A’ having the least impact on the search times were clustered into C1 and C2, respectively. This was expected since wearing headphones ‘A’ resulted in significantly larger localization errors, even though its design (i.e., open headphones) should provide a high level of transparency (Lladó et al., 2022b). Moreover, we obtained a larger separation between the cluster centers for the transition in the search times from cluster C2 to C3 and then from C3 to C4. This clear separation can be related to the differences in the mechanical designs of the corresponding headphones, which introduce different degradations of the acoustic features. The set of devices in cluster C3 were earbuds, open-back headphones and in-ear headphones with active hear-through. These slightly modify the pinnae cues, showing a large impact compared to an open headphones design (i.e., as in cluster C2) but a smaller difference in search times compared to cluster C4 – hear-through headphones and hearing protection devices that completely cover the pinnae. Headphones ‘J’ seems to be an exception because it was a hear-through circumaural headphones clustered into C3. Interestingly, the GMM yielded two additional clusters, C5 and C6, which did not find a correspondence to a specific headphones design. These clusters account for the very long search times observed in some trials, where the source was particularly difficult to find, i.e., localization of elevated or rear sources. This extreme degree of degradation was only perceived by a subset of subjects (see Figure 6), and the results from the cross-validation suggest considering C5 and C6 an idiosyncrasy in the collected behavioral data.

Our leave-one-out cross-validation showed that the prediction of the DLD score by means of the simulations done with the auditory model were correctly classified in eleven out of thirteen conditions. This high rate of correct classification results from the stability of the cluster parameters across validation conditions (see Table 2) and indicates that the estimated clusters were stable even when headphones were removed from our dataset. However, we did find a misclassification of the headphones ‘B’ and ‘E’, both members of the C3 cluster. Headphones ‘B’ showed a large variance across listeners in the behavioral task (see Figure 2) and adopting the manikin’s HRTF may not represent our pool of listeners for this headphone. Similarly, the misclassification for headphones ‘E’ may result in the incapability of the classifier to discriminate between the third and fourth clusters which both already indicate a high degree of degradation. Although our classification method is limited in such borderline cases, its high classification rate suggests high stability when classifying novel unseen headphones.

4.2 Limitations and future directions

The proposed model considers several aspects in modelling the behavioral mechanism, such as a slow temporal integration of spatial cues (Hofman and Van Opstal, 1998; Kayser et al., 2015). However, the model does not consider listeners’ anatomical constraints in head rotations yet (Gilman et al., 1979). For the present task, the high correlation between behavioral and predicted degradation scores suggests this simplification to be appropriate. Different experimental tasks may require further consideration of anatomical constraints.

Our methods can be adapted or expanded to other scenarios and experimental tasks to provide deeper insights into the degradation level for AR applications. It can be used to model more detailed search strategies or to consider alternative behavioral paradigms, such as navigation. Depending on the scope, it might be required to extend the auditory model. In the first step, the model could be extended to account for natural head rotations. Interestingly, most of the available models of head rotation do not consider auditory targets (Thurlow et al., 1967; Glenn and Vilis, 1992; Ceylan et al., 2000; Kunin et al., 2007). However, recent literature showed how head rotations might influence the dynamic computation of the acoustic cues (Macpherson, 2013; McLachlan et al., 2023). This integration would help the virtual agent to exploit the localization cues similarly as humans do (McLachlan et al., 2021). Furthermore, such an extended model may also help in providing more insights into the interplay between the sensory accumulation and decision-making process on a finer time scale (Kayser et al., 2015). These extensions could allow quantifying the degree of degradation on a trial-by-trial basis instead of relying on averaged search times. With the availability of such more complex auditory models, it would be possible to gain deeper insights into the interaction between the environment and listeners, and it would provide a quantitative approach for better headphones designs.

From an application point of view, our methodology is ready to be integrated into the development pipeline of headphones for AR, or HWDs in general. Our methodology can be applied to pre-select prototypes likely to be successful when tested in behavioral experiments. This could be beneficial to evaluate the quality of experience in an early stage of development (Nagele et al., 2021; Schneiderwind et al., 2021). Alternatively, our method could be adapted to consider the effects of acoustic individualization (Daugintis et al., 2023) of wearable devices when individually measured HRTFs are available. Thus, listener-specific HRTFs and localization data could be included to fine-tune the model to predict listener’s individual degradation caused by a specific headphone. This would further personalize the rendering scheme of a specific pair of headphones or even identify specific requirements for individual listeners.

5 Conclusion

We proposed a method to automatically predict the headphone-induced increase in search time in an auditory-guided visual search task, which presents a higher ecological validity than static sound-source localization tests. The proposed dynamic localization degradation (DLD) score was designed to cluster the search times for a pair of headphones automatically. The method is based on an auditory model simulating the behavioral task and predicting the DLD score. Our predictions were tested in a cross-validation with the actual DLD scores from the behavioral experiments.

The cross-validation method showed a high rate of correct classifications for unseen headphones indicating a high robustness of our method even with unseen headphones. The obtained clusters depended on the openness of the headphones design, suggesting that designs that maintain the listener’s pinnae cues are more suitable for AR scenarios in which space perception is an important aspect of the application. Our method is ready to be extended for listener-specific assessments, e.g., when accounting for individually measured HRTFs.

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