Granular Synthesis of Sound Textures using Statistical Learning

Ziv Bar-Joseph, Dani Lischinski, Michael Werman, Shlomo Dubnov, Ran El-Yaniv

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


We present a statistical learning algorithm for synthesizing random sound textures resembling an input sound texture segment. Our approach begins by constructing a hierarchical multi-resolution representation of the input signal. The resulting tree data structure is then statistically sampled to generate a new tree from which the output sound texture is reconstructed. This method works for both periodic and stochastic sounds and for mixtures of both, without assuming any explicit model for the data. Our results indicate that the proposed technique is effective and robust.

Original languageAmerican English
Pages (from-to)178-181
Number of pages4
JournalInternational Computer Music Conference, ICMC Proceedings
StatePublished - 1999
Event25th International Computer Music Conference, ICMC 1999 - Beijing, China
Duration: 22 Oct 199927 Oct 1999

Bibliographical note

Funding Information:
This research was supported in part by the Israel Science Foundation founded by the Israel Academy of Sciences and Humanities. Ran El-Yaniv is a Marcella S. Geltman Memorial Academic Lecturer.

Publisher Copyright:
© 1999 International Computer Music Conference, ICMC Proceedings. All rights reserved.


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