Musical timbre cognition
Is musical timbre just a fleeting sensation or is it fully retained in memory? What are the underlying cognitive mechanisms? Answers to these question might not only inform the psychological underpinnings of this musical parameter, but also provide a psychological basis for the study of musical phenomena such as “timbre-melody”, drum tracks, sound structure in electronic music, … As part of my PhD research, this work was conducted under the supervision of Stephen McAdams in the McGill Music Perception and Cognition Lab.
Here’s a talk on early portions of the project (UC Berkeley Redwood Institute for Theoretical Neuroscience seminar, Dec 2013).
And a talk on later portions of the project (Berlin Interdisciplinary Workshop on Timbre, Jan 2017).
K. Siedenburg & S. McAdams: “The role of long-term familiarity and attentional maintenance in short-term memory for timbre”. Memory, 25(4), pp. 550-564
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
Modeling Perceptual Audio Dissimilarity
I am interested in the acoustic and cognitive factors that affect audio and timbre dissimilarity perception. What are the most relevant acoustic features? How can these be subsumed in reliable statistical models?
K. Siedenburg & D. Müllensiefen (2017). Modeling timbre similarity of short music clips. Frontiers in Psychology (Section Cognition), 8:639, doi: 10.3389/fpsyg.2017.00639
K. Siedenburg, I. Fujinaga, S. McAdams: “A Comparison of Approaches to Timbre Descriptors in Music Information Retrieval and Music Psychology”, Journal of New Music Research, 45, pp. 27-42, Jan 2016
K. Siedenburg, K. Jones-Mollerup, S. McAdams: “Acoustic and categorical dissimilarity of musical timbre: Evidence from asymmetries between acoustic and chimeric sounds”. Frontiers in Psychology, 6:1977, doi: 10.3389/fpsyg.2015.01977. Jan 2016
Structured Time-Frequency Processing and Audio Enhancement
This project attempts to exploit the inherent structure of music and speech signals in order to more reliably estimate their time-frequency content. This project has grown out of an involvement with techniques of structured sparsity in my MSc (“Diplom”) thesis and is currently gravitating towards applications such as audio noise removal and audio declipping.
I’ve create a MATLAB toolbox on structured sparsity and generalized time-frequency thresholding.
M. Kowalski, K. Siedenburg, M. Dörfler: “Social Sparsity! Neighborhood Structures Enrich Structured Shrinkage Operators”, IEEE Transactions on Signal Processing, 61(10), p. 2498-2511, May 2013
K. Siedenburg & M. Dörfler: “Persistent Time-Frequency Shrinkage for Audio Denoising”, Journal of the Audio Engineering Society (AES), No. 61 (1/2), Jan/Feb 2013