Signal Processing

Kai Siedenburg



Kai Siedenburg
Dept. of Medical Physics and Acoustics - Signal Processing Group 
University of Oldenburg 
D-26111 Oldenburg 

Tel: +49-441-798 3312

Fax: +49-441-798 3902
Office: W30 2-212



Kai Siedenburg studied Mathematics and Musicology at Humboldt University Berlin and as a Fulbright visiting student at the University of California, Berkeley. In 2012, he worked at the Austrian Research Institute for Artificial Intelligence in Vienna. He subsequently obtained his PhD in Music Technology from McGill University, Montreal. Since Dec 2015, he is postdoctoral fellow in the Signal Processing Group at the University of Oldenburg.

Research Interests

  • Music perception and hearing loss
  • Perceptual and computational auditory scene analysis
  • Timbre perception and cognition
  • Time-frequency signal processing for audio enhancement

Journal papers

  1. K. Siedenburg & S. McAdams (2017). Four conceptual distinctions for the auditory ‘wastebasket’ of timbre. Frontiers in Psychology (Auditory Cognitive Neuroscience), doi: 10.3389/fpsyg.2017.01747
  2. K. Siedenburg & D. Müllensiefen (2017). Modeling timbre similarity of short music clips. Frontiers in Psychology (Cognition), 8:639, doi: 10.3389/fpsyg.2017.00639
  3. K. Siedenburg & S. McAdams (2017). The role of long-term familiarity and attentional maintenance in short-term recognition of musical timbre. Memory, 25(4), 550–564
  4. K. Siedenburg, S. Mativetsky, S. McAdams (2016). Auditory and verbal memory in North Indian tabla drumming. Psychomusicology: Music, Mind, and Brain, 26 (4), pp. 327–336
  5. K. Siedenburg, K. Jones-Mollerup, S. McAdams (2016). Acoustic and categorical dissimilarity of musical timbre: Evidence from asymmetries between acoustic and chimeric sounds. Frontiers in Psychology (Auditory Cognitive Neuroscience), 6:1977, doi: 10.3389/fpsyg.2015.01977
  6. K. Siedenburg, I. Fujinaga, S. McAdams (2016). A Comparison of Approaches to Timbre Descriptors in Music Information Retrieval and Music Psychology. Journal of New Music Research, 45(1), pp. 27–41
  7. M.Kowalski,K.Siedenburg,M.Dörfler(2013).SocialSparsity!NeighborhoodStructuresEnrichStructuredShrin- kage Operators, IEEE Transactions on Signal Processing, 61(10), pp. 2498-2511
  8. K. Siedenburg & M. Dörfler (2013). Persistent Time-Frequency Shrinkage for Audio Denoising. Journal of the Audio Engineering Society (AES), No. 61 (1/2)

Book chapters

  1. S. McAdams & K. Siedenburg (2018, forthcoming). Perception and cognition of musical timbre. In P. J. Rentfrow & D. J. Levitin (Eds.), Foundations of Music Psychology: Theory and Research. Cambridge, MA: MIT Press

  2. K. Siedenburg (2017). Instruments unheard of: On the role of familiarity and sound source categories in timbre perception. In T. Bovermann, A. de Campo, H. Egermann, S. Indriyati-Hardjowirogo, and S. Wein- zierl (Eds.), Musical Instruments in the 21st Century: Identities, Configurations, Practices (pp. 385-396). Heidelberg, Germany: Springer 

Conference papers

  1. K. Siedenburg & S. Doclo (2017). Iterative structured shrinkage algorithms for stationary/transient audio separati- on. 20th International Conference on Digital Audio Effects (DAFx-17), Edinburgh, UK, Sep 5–8, 2017 [Best Paper Award (1st Prize)]
  2. B. Cauchi, J. F. Santos, K. Siedenburg, T. H. Falk, P. A. Naylor, S. Doclo and S. Goetze (2016). Predicting the quality of processed speech by combining modulation based features and model-trees. Proceedings of the 2016 ITG Conference on Speech Communication, Paderborn, Germany, Oct 5–7, 2016
  3. K. Siedenburg, M. Dörfler, M. Kowalski: “Audio Declipping with Social Sparsity”. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, May 4–8 2014
  4. K. Siedenburg & P. Depalle: “Modulation Filtering for Structured Time-Frequency Estimation of Audio Signals”. Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz NY, USA, October 20–23 2013
  5. K. Siedenburg: “Persistent Empirical Wiener Estimation with Adaptive Threshold Selection for Audio Denoising”. Proceedings of the 9th Sound and Music Computing Conference, Copen- hagen, Denmark, July 11–14 2012
  6. K. Siedenburg & M. Dörfler: “Audio Denoising by Generalized Time-Frequency Thresholding”. Proceedings of the AES 45th Conference on Applications of Time-Frequency Processing in Audio, Helsinki, Finland, March 1–3 2012
  7. K. Siedenburg & M. Dörfler: “Structured Sparsity for Audio Signals”. Proceedings of the 14th International Conference on Digital Audio Effects, DAFx-11, Paris, France, September 2–9 2011
  8. K. Siedenburg: “An Exploration of Real-Time Visualizations of Musical Timbre”. Proceedings of the 3rd International Workshop on Learning Semantics of Audio Signals, Graz, Austria, December 1 2009 


Musikwahrnehmung und Kognition, WS 2017/18