Exploiting the structures of audio signals as persistence along time and frequency leads to significant improvements of classical audio denoising algorithms. This talk gives an overview of current research activities in the field of structured sparsity applied to audio denoising. Of particular interest will be shrinkage operators which take into account a signal's persistence properties. It will turn out that they may serve as an efficient alternative to state of the art algorithms both in terms of denoising performance measured in signal to noise ratio and perceptual qualities. Furthermore, a model for adaptively choosing the shrinkage threshold will be presented, as well as the operators' relations to convex minimization problems.

Kai Siedenburg studied Mathematics and Musicology at Humboldt University Berlin, receiving his M.Sc. degree in 2012. In 2008/09 he visited the University of California, Berkeley and its Center For New Music and Audio Technologies (CNMAT) on a Fulbright scholarship. He further worked as student research assistant at the Audio Communication Group at the Berlin Institute of Technology in 2009-2011. His masters thesis on sparse modeling of audio signals was realized at the Numerical Harmonic Analysis Group (NUHAG) Vienna. Throughout his studies Kai was stipendiary of the German National Academic Merit Foundation. Currently, he is working on audio signal processing at the Austrian Research Institute for Artificial Intelligence (OFAI). Kai's academic interests mainly focus on the (cognitive / computational / mathematical) representation and synthesis of musical sounds. He is particularly interested in mathematical models of audio signals, as well as musical timbre, from musicological and perceptual viewpoints. Artistically, he is active as a Jazz-pianist and electronic musician, playing groove-Jazz and being engaged in free improvisation and the design of electronic instruments.