1. Progressive Perceptual Audio Rendering of Complex Scenes Thomas Moeck - Nicolas Bonneel - Nicolas Tsingos - George Drettakis - Isabelle Viaud-Delmon - David Alloza 1- REVES/INRIA Sophia-Antipolis 2- Computer Graphics Group, University of Erlangen-Nuremberg 3- CNRS-UPMC UMR 7593 4- EdenGames 1,2 1 1 1 3 4

2. Objectives Efficient audio rendering of very complex scenes with moving sources Without audible impairment of the quality Verify results by user tests

3. Previous Work Rendering complex auditory scenes Clustering [Tsingos et al. 2004]: replace many sources with a representative Still can only treat ~200 sound sources (cost of clustering itself) Scalable audio processing Importance-guided processing of few frequency/time bins [Fouad et al. 1997, Wand & Straßer 2004, Gallo et al. 2005, Tsingos 2005]. Audio processing (e.g., HRTF, spatialization) is expensive Crossmodal effects Neuroscience Literature: Ventriloquism affects 3D audio perception Ventriloquism spatial window can vary from a few up to 15 degree Few papers on ecological experiments

4. Methodology Recursive approach to clustering Reduce cost of clustering Scalable perceptual premixing Faster premixing without audible loss of quality Taking perceptual and cross-modal information into account Improve audio clustering algorithm User experiments to detect improvement possibilities Improving quality with results of tests Validation of resulting algorithms

5. Overview of the algorithms Masking of inaudible sources (with energy) Clustering of remaining sources Progressive premixing within each cluster Spatial audio processing (HRTF) recursiv e

6. Our Work Optimized recursive approach of clustering Clustering performance evaluation Improved scalable perceptual premixing Quality evaluation study Study of cross-modal effects by user experiments Using results of cross-modal studies to develop audio-visual clustering algorithm

7. Optimized Recursive Clustering Recursive splitting of clusters Fixed-budget approach Using a fixed number of clusters Variable-budget approach Splitting clusters until break condition is reached Break condition: Average angle error Optimal number of clusters Variant used by EdenGames 8 cluster budget Local clustering when necessary

8. Eden Games implementation Test Drive Unlimited

9. Clustering Performance Evaluation Performance of recursive algorithms are clearly better

10. Improved progressive scalable perceptual premixing (1) After clustering: Premixing in each cluster Why? Effects can be done afterwards - less cost because viewer signals Only premixing necessary data Assigning frequency bins to sound sources (iterative importance sampling) by using pinnacle value

11. Improved progressive scalable perceptual premixing (2) premixing clustering

12. Improved progressive scalable perceptual premixing (3) Iterative importance sampling Calculation of importance value from energy, loudness or audio saliency map Assignment of frequency proportional to importance until pinnacle value is reached Reassignment of remaining frequencies to sounds relative to importance values

13. Varying budget

14. Quality Evaluation Study (1) MUSHRA (Multiple Stimuli with Hidden Reference and Anchors) test of perceptual premixing 7 subjects, aged from 23 – 40 Ambient, music and speech Various budgets (2% – 25 %) With and without pinnacle value Using loudness or saliency as importance value

15. Quality Evaluation Study (2) Results: Approach is capable of generating high quality using 25% of the original data Acceptable results with 10% (2% in case of speech) Significant Effects: Budget Importance value Pinnacle value

16. Study of Cross-Modal Influences – Questions Do we need more or fewer clusters in the viewing frustum? We move spatial position of sound sources to representative in cluster How tolerant are we to this error ? Do visuals influence the perceived quality?

17. Study of Cross-Modal Influences – Setup (1)

18. Study of Cross-Modal Influences – Setup (2)

19. Study of Cross-Modal Effects – Setup (3)

20. Uniform distribution [1/4]

21. [2/3] condition

22. [3/2] condition

23. [4/1] condition

24. Study of Cross-Modal Influences – Results Statistical analysis of the results shows: We need more clusters in the viewing frustum No significant difference of visuals/no-visuals but possible cross-modal effect

25. Modifying the algorithm Introducing weighting term in clustering: Increasing number of clusters in the viewing frustum

26. Cross-Modal illustration

27. Video: Putting it all together

28. Conclusions Up to nearly 3000 sound sources possible in good quality Main limitation are graphics (!) Better quality because more clusters in viewing frustum Future work experiment with auditory saliency measurements handle procedurally synthesized sounds?

29. Questions?