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HOW DO HUMANS LOCALIZE A SOUND SOURCE?

THE MANIFOLDS OF SPATIAL HEARING Ramani Duraiswami | Vikas C. Raykar Perceptual Interfaces and Reality Lab University of Maryland, College park. HOW DO HUMANS LOCALIZE A SOUND SOURCE?. Primary cues Interaural Time Difference (ITD) Interaural Level Difference (ILD)

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HOW DO HUMANS LOCALIZE A SOUND SOURCE?

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  1. THE MANIFOLDS OF SPATIAL HEARINGRamani Duraiswami | Vikas C. RaykarPerceptual Interfaces and Reality Lab University of Maryland, College park

  2. HOW DO HUMANS LOCALIZE A SOUND SOURCE? • Primary cues • Interaural Time Difference (ITD) • Interaural Level Difference (ILD) • Can explain only localization in the horizontal plane. • All points in the cone of confusion have the same ITD and ILD. • Other cues • Pinna shape gives elevation cues for higher frequencies. • Torso and Head give elevation cues for lower frequencies.

  3. Source HEAD Left ear Right ear

  4. HRTF • The spectral filtering caused by the head, torso and the pinna can be described by the Head Related Transfer Function (HRTF). • Can experimentally measure HRTF for all elevation and azimuth for both ears for different persons. Convolve the source signal with the measured HRIR to create virtual audio

  5. Sample HRIR and HRTF

  6. MANIFOLD REPRESENTATION • A HRIR of N samples can be considered as a point in N dimensional space. • As the elevation is varied smoothly, the points essentially trace out a one-dimensional manifold in the N-dimensional space.

  7. PERCEPTUAL MANIFOLDS • In the N dimensional Euclidean space of the original HRIRs, two HRIRs corresponding to far apart elevations may still be very close to each other. • However on the one-dimensional manifold, where we measure the distance between two points as the length of the geodesic on the manifold, they are far apart. • If we can unfold this low-dimensional manifold we have a good perceptual representation of the signal.

  8. PCA and MDS see the Euclidean distance A What is important is the geodesic distance Unroll the manifold

  9. NON-LINEAR MANIFOLD LEARNING • Nonlinear manifold techniques essentially help to unfold the manifold giving a low dimensional representation. • Isomap and Locally Linear Embedding (LLE) are two popular techniques. • We use the LLE, since it has a good representational capacity and does not make any assumptions regarding manifold structure.

  10. LLE • LLE models local neighborhoods as linear patches and then embeds them in a lower dimensional manifold .

  11. FIT LOCALLY • We expect each data point and its neighbors to lie on or close to a locally linear patch of the manifold. • Each point can be written as a linear combination of its neighbors. • The weights chosen to minimize the reconstruction Error.

  12. CRUX OF LLE • The weights that minimize the reconstruction errors are invariant to rotation, rescaling and translation of the data points. • The same weights that reconstruct the data points in D dimensions should reconstruct it in the manifold in d dimensions. • The weights characterize the intrinsic geometric properties of each neighborhood

  13. THINK GLOBALLY

  14. HRIR/HRTF MANIFOLDS • We used the public domain CIPIC database. • We tried to recover the elevation manifolds for both the HRIR and the HRTF with K=2 neighbors.

  15. HRIR MANIFOLD The one-dimensional HRIR manifold recovered by the LLE technique using K=2 neighbors. The same manifold embedded in three dimensions recovered using K=4 neighbors is also shown.

  16. HRTF MANIFOLD The one-dimensional HRTF manifold recovered by the LLE technique using K=2 neighbors. The same manifold embedded in three dimensions recovered using K=4 neighbors is also shown.

  17. CHOICE K • The only free parameter that needs to be selected in the LLE algorithm is the number of neighbors K. • Large K leads to short-circuit errors. • Small K leads to fragmented manifold. • K related to the intrinsic dimensionality.

  18. DIFFERENT K The manifold recovered for different values of K.

  19. DIFFERENT AZIMUTHS

  20. DIFFERENT SUBJECTS

  21. A NEW DISTANCE METRIC • How to compare any two given HRIRs i.e. how to formulate a distance metric in the space of HRIRs. • The distance metric has to be perceptually inspired. • The absolute justification however is to do psycho acoustical tests. • In the absence of any good perceptual error metric the most commonly used one is the squared log-magnitude error of the spectrum of the HRIRs.

  22. DISTANCE ON THE MANIFOLD • It is tough to decide what aspects of a given signal are perceptually relevant. • For our case of all HRIRs for different elevation angles, the obvious perceptual information to be extracted is the elevation of the source. • A natural measure of distance would be the distance on the extracted one dimensional manifold

  23. (a) The distance matrix using the metric defined in Equation 1 (b) using the distance on the manifold.

  24. HRIR INTERPOLATION • It is also possible to go from the manifold to the signal representation. • If we want the HRTF for a new elevation we find the value of the lower-dimensional manifold at the required angle . • Once we know the value on the manifold it can be written as a linear combination of its neighbors and compute the weights that best linearly reconstructs it from its neighbors. • The same weights reconstruct the HRTF in the higher dimensional space.

  25. The actual and the reconstructed HRTF for elevation 0o and azimuth 0o.

  26. CONCLUSION • We presented a new representation for the HRTFs in terms of the elevation manifold they lie on. • We also proposed a new distance metric and a new scheme for HRIR interpolation.

  27. REFERENCES • S. Roweis and L. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science, vol. 290, pp. 2323– 2326, december 2000. • J.P. Blauert, Spatial Hearing (Revised Edition), MIT Press, Cambridge, MA, 1997. • J. C. Middlebrooks and D. M. Green, “Sound localization by human listeners,” Annual Review of Psychology, vol. 42, pp. 135–159, 1991. • F. L. Wightman and D. J. Kistler, “Monaural sound localization revisited,” Journal of the Acoustical Society of America,vol. 101, no. 2, pp. 1050–1063, Feb. 1997. • H. S. Seung and D. D. Lee, “The manifold ways of perception,” Science, vol. 290, pp. 2268, december 2000. • J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science, vol. 290, pp. 2319–2323, december 2000. • V. de Silva and J. B. Tenenbaum, “Local versus global methods for nonlinear dimensionality reduction,” Advances in Neural Information Processing Systems, vol. 15, 2003. • V. R. Algazi, R. O. Duda, D. M. Thompson, and C. Avendano, “The CIPIC HRTF database,” Proc.2001 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, Mohonk Mountain House, New Paltz, NY, pp. 99–102, October 2001. • M. Balasubramanian, E. L. Schwartz, J. B. Tenenbaum, V. de Silva, and J. C. Langford, “The isomap algorithm and topological stability,” Science, vol. 295, 2002.

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