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 currently is an Associated Junior Fellow at the Hanse Institute for Advanced Studies, and holds a Marie Sklodowska-Curie Individual Postdoctoral Fellowship (2018-2020). He studied Mathematics and Musicology at Humboldt University Berlin and as a Fulbright visiting student at the University of California, Berkeley. He obtained his PhD in Music Technology from McGill University, Montreal, and the Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT). His work has gained recognition by institutions such as the European Commission (H2020), the German Academic Exchange Service, the Canadian Auditory Cognitive Neuroscience Training Network, and the Audio Engineering Society. Recently, he received the best paper award at the 2017 Int. Conf. on Digital Audio Effects (DAFX) in Edinburgh, UK.

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 (in press). Short-term recognition of timbre sequences: Musical training, pitch variability, and timbral similarity. Music Perception
  2. K. Siedenburg (2018). Timbral Shepard-illusion reveals perceptual ambiguity and context sensitivity of brightness perception. The Journal of the Acoustical Society of America, 143(2), EL-00691
  3. 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
  4. K. Siedenburg & D. Müllensiefen (2017). Modeling timbre similarity of short music clips. Frontiers in Psychology (Cognition), 8:639, doi: 10.3389/fpsyg.2017.00639
  5. 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
  6. 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
  7. 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
  8. 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
  9. M.Kowalski,K.Siedenburg,M.Dörfler(2013).SocialSparsity!NeighborhoodStructuresEnrichStructuredShrin- kage Operators, IEEE Transactions on Signal Processing, 61(10), pp. 2498-2511
  10. 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