1. Creating Sound Texture through Wavelet Tree Learning and ModelingBy
Guy Shterlich
Asaf Kacen
Supervisor
Dr. Shlomo Dubnov

2. Motivation and Background
In simple words:
create a synthesis sound texture from an original sample.
What for?
automatic generation of sound effects.
creative musical and sonic manipulations.
virtual reality sonification.
Why?
Simple repetition is not efficient and sounds artificial.
Automation.
Low storage.

3. Process steps
Input:
sound signals that are approximately stationary: rain, waterfall, traffic noises, people babble, machine noises, etc.,
Discrete Wavelet Transform:
construct a tree, representing a hierarchical wavelet transform of the signal.
Synthesis algorithm:
new random trees are generated by learning and sampling the conditional probabilities of the paths in the original tree.
Inverse DWT:
Transformation of these random trees back into signals results in new sound textures that closely resemble the sonic impression of the original sound source but without exactly repeating it.

4. Discrete Wavelet Transform
Original signal
Scaling filter Downsample x2
Wavelet filter Downsample x2
Scaling filter Downsample x2
Wavelet filter Downsample x2
Scaling Down x2
Wavelet Down x2
scaling (lev 0)
detail (lev 1)
detail coefficients (level 2)
detail coefficients (level 3)

5. Tree Synthesis algorithm
Original tree
new tree ?

6. Tree Synthesis algorithm
Original tree
new tree ?

7. Candidate set
Original tree
new tree ?

8. Candidate set
Original tree
new tree ?

9. For i=1 to length(candidate set)
Original tree
new tree ?

10. Anc Candidate
Original tree
new tree ?

11. Anc Candidate
Original tree
new tree ?

12. Anc Candidate
Original tree
new tree ?

13. Pre Candidate
Original tree
new tree ?

14. Pre Candidate
Original tree
new tree ?

15. Pre Candidate
Original tree
new tree ?

16. The winners are…
Original tree
new tree ?

17. Randomly choose
Original tree
new tree ?

18. Inherit candidates
Original tree
new tree

19. Inherit candidates
Original tree
new tree

20. CONTROL!
Threshold K
Originality
Creativity
Noisiness
threshold

21. Demonstration
skip

26. Conclusions & Future work
Algorithm limitation:
Sound texture types dependency.
Require delicate and fine control (automation!).
Noise and distortions Vs. creativity .
Artifacts in the reconstructed signal caused by even small changes in the wavelet domain.
Future work:
Enhance predecessor candidate algorithm .
Larger Input.
Research and uses of other wavelet types.
GUI improvements.

27. Questions ?