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The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Acoustic and non-acoustic factors in modeling listener-specific performance of sagittal-plane sound localization

Majdak, Piotr, Baumgartner, Robert, Laback, Bernhard·2014·Frontiers in Psychology
DOI 10.3389/fpsyg.2014.00319PMC PMC4006033
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Abstract

The ability of sound-source localization in sagittal planes (along the top-down and front-back dimension) varies considerably across listeners. The directional acoustic spectral features, described by head-related transfer functions (HRTFs), also vary considerably across listeners, a consequence of the listener-specific shape of the ears. It is not clear whether the differences in localization ability result from differences in the encoding of directional information provided by the HRTFs, i.e., an acoustic factor, or from differences in auditory processing of those cues (e.g., spectral-shape sensitivity), i.e., non-acoustic factors. We addressed this issue by analyzing the listener-specific localization ability in terms of localization performance. Directional responses to spatially distributed broadband stimuli from 18 listeners were used. A model of sagittal-plane localization was fit individually for each listener by considering the actual localization performance, the listener-specific HRTFs representing the acoustic factor, and an uncertainty parameter representing the non-acoustic factors. The model was configured to simulate the condition of complete calibration of the listener to the tested HRTFs. Listener-specifically calibrated model predictions yielded correlations of, on average, 0.93 with the actual localization performance. Then, the model parameters representing the acoustic and non-acoustic factors were systematically permuted across the listener group. While the permutation of HRTFs affected the localization performance, the permutation of listener-specific uncertainty had a substantially larger impact. Our findings suggest that across-listener variability in sagittal-plane localization ability is only marginally determined by the acoustic factor, i.e., the quality of directional cues found in typical human HRTFs. Rather, the non-acoustic factors, supposed to represent the listeners' efficiency in processing directional cues, appear to be important.

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Figures

Figure 1 Structure of the sound localization model from Baumgartner et al. (2013). The incoming target sound is peripherally processed and the result is compared to an internal template set. The comparison result is mapped yielding the probability for responding at a given polar angle. The blue arrows indicate the free parameters of the corresponding sections. In the model, the DTF set and the uncertainty represent the acoustic and non-acoustic factors, respectively.
Figure 1 Structure of the sound localization model from Baumgartner et al. (2013). The incoming target sound is peripherally processed and the result is compared to an internal template set. The comparison result is mapped yielding the probability for responding at a given polar angle. The blue arrows indicate the free parameters of the corresponding sections. In the model, the DTF set and the uncertainty represent the acoustic and non-acoustic factors, respectively.CC BY · PubMed Central
Figure 2 Actual and modeled localization. Actual localization responses (circles) and modeled response probabilities (PMVs, brightness encoded) calculated for three uncertainties U and four exemplary listeners indexed by k.
Figure 2 Actual and modeled localization. Actual localization responses (circles) and modeled response probabilities (PMVs, brightness encoded) calculated for three uncertainties U and four exemplary listeners indexed by k.CC BY · PubMed Central
Figure 3 Predicted localization performance depends on the uncertainty. PEs and QEs are shown as functions of U for four exemplary listeners (k = 3: blue squares, k = 9: red triangles, k = 12: green diamonds, k = 15: black circles). Lines show the model predictions. Symbols show the actual performance obtained in the localization experiment (placement on the abscissa corresponds to the optimal listener-specific uncertainty Uk).
Figure 3 Predicted localization performance depends on the uncertainty. PEs and QEs are shown as functions of U for four exemplary listeners (k = 3: blue squares, k = 9: red triangles, k = 12: green diamonds, k = 15: black circles). Lines show the model predictions. Symbols show the actual performance obtained in the localization experiment (placement on the abscissa corresponds to the optimal listener-specific uncertainty Uk).CC BY · PubMed Central
Figure 4 Predicted versus actual localization performance. Predicted PEs and QEs are shown as functions of the actual PEs and QEs, respectively, for each listener. (A) Optimal listener-specific uncertainties Uk. (B) Listener-constant uncertainty yielding best correlation for PE, U = 2.89. (C) Listener-constant uncertainty yielding best correlation for QE, U = 1.87. (D) Listener-constant uncertainty from (Langendijk and Bronkhorst, 2002), U = 2.0. (E) Listener-specific uncertainties Uk and the same DTF set (k = 14) for all listeners (see Section 3.3 for more details). The correlation coefficient is denoted by r.
Figure 4 Predicted versus actual localization performance. Predicted PEs and QEs are shown as functions of the actual PEs and QEs, respectively, for each listener. (A) Optimal listener-specific uncertainties Uk. (B) Listener-constant uncertainty yielding best correlation for PE, U = 2.89. (C) Listener-constant uncertainty yielding best correlation for QE, U = 1.87. (D) Listener-constant uncertainty from (Langendijk and Bronkhorst, 2002), U = 2.0. (E) Listener-specific uncertainties Uk and the same DTF set (k = 14) for all listeners (see Section 3.3 for more details). The correlation coefficient is denoted by r.CC BY · PubMed Central
Figure 5 Localization performance depends on the uncertainty and DTF set. Predicted PEs and QEs as functions of the uncertainty of k-th listener (Uk) and DTF set of k-th listener (Dk).
Figure 5 Localization performance depends on the uncertainty and DTF set. Predicted PEs and QEs as functions of the uncertainty of k-th listener (Uk) and DTF set of k-th listener (Dk).CC BY · PubMed Central
Figure 6 Listener-specific performance depends on the DTF set used in the model. ΔPEs and ΔQEs averaged over all Uks as a function of Dk. ΔPEs and ΔQEs are the PEs and QEs relative to the listener-specific PEs and QEs, respectively. The whiskers show ±1 SD.
Figure 6 Listener-specific performance depends on the DTF set used in the model. ΔPEs and ΔQEs averaged over all Uks as a function of Dk. ΔPEs and ΔQEs are the PEs and QEs relative to the listener-specific PEs and QEs, respectively. The whiskers show ±1 SD.CC BY · PubMed Central
Figure 7 DTF set contributes less than uncertainty to the performance variability of the group. PE SDs and QE SDs as functions of either listener-constant DTF set calculated for listener-specific uncertainties (Uk varied, blue squares) or the listener-constant uncertainty calculated for listener-specific DTF sets (DTF varied, red circles). The abscissa is sorted by the ascending listener-specific uncertainty Uk.
Figure 7 DTF set contributes less than uncertainty to the performance variability of the group. PE SDs and QE SDs as functions of either listener-constant DTF set calculated for listener-specific uncertainties (Uk varied, blue squares) or the listener-constant uncertainty calculated for listener-specific DTF sets (DTF varied, red circles). The abscissa is sorted by the ascending listener-specific uncertainty Uk.CC BY · PubMed Central

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Metadata

Authors
Majdak, P., Baumgartner, R., & Laback, B.
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2014-04-23
Volume
5
ISSN
1664-1078
DOI
10.3389/fpsyg.2014.00319
PMC
PMC4006033
Cited by
39
References
27

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CoChat. (2026). Citation check: Acoustic and non-acoustic factors in modeling listener-specific performance of sagittal-plane sound localization. https://app.cochat.ai/papers/10.3389/fpsyg.2014.00319
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Majdak, P., Baumgartner, R., & Laback, B. (2014). Acoustic and non-acoustic factors in modeling listener-specific performance of sagittal-plane sound localization. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2014.00319

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