On the Preprocessing and Postprocessing of HRTF Individualization Based on Sparse Representation of Anthropometric Features Jianjun HE, Woon-Seng Gan, and Ee-Leng Tan 24 th April 2015 Digital Signal Processing Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

WHY 2 1.Head-related transfer functions (HRTFs) are highly individualized; 2.HRTFs are closely related to Anthropometry (torso, head, pinna); 3.Anthropometry can be used for HRTF individualization. Individualization Anthropometry of a new person HRTF of the new person Anthropometry database HRTF database In this paper, we aim to answer: 1.Whether the preprocessing and postprocessing methods affect the performance of HRTF individualization? 2.If so, what is the best preprocessing and postprocessing combination? 3.And, how good is it?

CIPIC Anthropometric data (35 subjects) 3 V. R. Algazi, R. O. Duda, D. M. Thompson, and C. Avendano, “The CIPIC HRTF database,” in Proc. IEEE WASPAA, New Paltz, NY, Oct

Methodology 4 Individualization A1A1 H1?H1? A H Anthropometry database A : S subjects * 1 set of Anthropometry (F features) Anthropometry of a new person A 1 : 1 subjects * 1 set of Anthropometry HRTF database H : S subjects * 1 set of HRTF (D directions * K points) HRTF of a new person H 1 ： 1 subjects * 1 set of HRTF P. Bilinski, J. Ahrens, M. R. P. Thomas, I. Tashev, and J. C. Plata, “HRTF magnitude synthesis via sparse representation of anthropometric features,” in Proc. IEEE ICASSP, Florence, Italy, pp , May 2014.

Methodology 5 Preprocessing of Anthropometry i 1.Direct 2.Min-max normalization 3.Standard score 4.Standard deviation normalization Preprocessing of HRTF m 1.Magnitude 2.Log magnitude 3.Power Sparse representation j 1.Direct 2.Nonnegative Postprocessing of Anthropometry l 1.Direct 2.Normalized In total, we have variants of methods!

Evaluation 6 CIPIC HRTF database; Cross validation technique to selection the regularization parameter; S test = 35 test cases, all 1250 directions, and full frequency range. Performance varies among different preprocessing and postprocessing methods! Sparse representation PostAPreH PreA DirectMin-max Standard score Standard deviation Direct Mag Log mag Power Normalized Mag Log mag Power Nonnegative Direct Mag Log mag Power Normalized Mag Log mag Power

Results 7 Direct sparse representation 1. PreA: standard deviation best, standard score worst; 2. PreH: log mag best, power worst; 3. PostA, PostH: minimal effect for good PreA, PreH. Nonnegative sparse representation 1. Better than corresponding direct sparse representation (especially for standard score); 2. Trend in PreA/PreH not obvious; 3. Normalized PostA can improve the performance (especially for standard score). (a) Direct sparse; Direct PostA (b) Direct sparse; Normalized PostA (c) Nonnegative sparse; Direct PostA (d) Nonnegative sparse; Normalized PostA

MethodSpecificationSD (dB) Single best Select one single set of HRTF with the corresponding closest anthropometry 8.11 Tashev et al Min-max PreA Magnitude PreH Direct sparse representation No reported postprocessing 6.57 Our best Standard score PreA Log magnitude PreH Nonnegative sparse representation Normalized PostA 5.86 Lower bound Linear regression based HRTF individualization 5.12 Comparison 8

Conclusions 9 1.Introduced preprocessing and postprocessing in HRTF individualization based on sparse representation of anthropometric features. 2.Investigated 48 variants of preprocessing and postprocessing methods, and found a)Preprocessing and postprocessing methods do affect the performance of HRTF individualization, though the effects differ in different combinations; b)Adding nonnegative constraints in sparse representation improves the performance; c)The best combination for HRTF individualization is. 3.Established the lower bound for this type of HRTF individualization and verified that “our best” combination outperforms existing approaches and is quite close to the lower bound. 4.Future work: subjective evaluation of HRTF individualization.

References 10 [1] D. R. Begault, 3-D Sound for Virtual Reality and Multimedia, Cambridge, MA: Academic Press, [2] J. Blauert, Spatial Hearing: The Psychophysics of Human Sound Localization, The MIT Press, revised edition, [3] H. Møller, “Fundamentals of Binaural Technology,” Applied Acoustics, vol. 36, , [4] W. G. Gardner, and K. D. Martin, “HRTF Measurements of a KEMAR,” J. Acoust. Soc. Amer., vol., vol. 97, pp , See also [5] H. Møller, M. F. Sørensen, D. Hammershøi, and C. B. Jensen, “Head-Related Transfer Functions of Human Subjects,” J. Aud. Eng. Soc., vol. 43, pp , [6] V. R. Algazi, R. O. Duda, D. M. Thompson, and C. Avendano, “The CIPIC HRTF database,” in Proc. IEEE WASPAA, New Paltz, NY, USA, Oct [7] E. M. Wenzel, M. Arruda, D. J. Kistler, and F. L. Wightman, “Localization Using Non-individualized Head- Related Transfer Functions,” J. Acoust. Soc. Amer., vol. 94, pp , [8] S. Xu, Z. Li, and G. Salvendy, “Individualization of Head-related transfer function for three-dimensional virtual auditory display: a review,” in R. Shumaker (Ed.): Virtual Reality, HCII 2007, LNCS 4563, pp. 397–407, [9] K. Sunder, J. He, E. L. Tan, and W. S. Gan, “Natural sound rendering for headphones,” IEEE Signal Processing Magazine, vol. 32, no.2, pp , Mar [26] P. Bilinski, J. Ahrens, M. R. P. Thomas, I. Tashev, and J. C. Plata, “HRTF magnitude synthesis via sparse representation of anthropometric features,” in Proc. IEEE ICASSP, Florence, Italy, pp , May [28] S. J. Kim, K. Koh, M. Lusig, S. Boyd, and D. Gorinevsky, “An interior-point method for large-scale l1- regularized least squares,” J. Selected topics in signal processing, vol. 1, no. 4, pp , Dec [30] J. Breebaart, F. Nater, and A. Kohlrausch, “Spectral and spatial parameter resolution requirements for parametric, filter-bank-based HRTF processing,” J. Audio Eng. Soc., vol. 58, no. 3, pp , Mar

Acknowledgement 11 THIS WORK IS SUPPORTED BY THE SINGAPORE MINISTRY OF EDUCATION ACADEMIC RESEARCH FUND TIER-2, UNDER RESEARCH GRANT MOE2010-T

On the Preprocessing and Postprocessing of HRTF Individualization Based on Sparse Representation of Anthropometric Features Jianjun HE Nanyang Technological University, Singapore Thank you!