Modal Decomposition of Feedback Delay Networks

Schlecht S, Habets E (2019)

Publication Type: Journal article

Publication year: 2019

Journal

IEEE Transactions on Signal Processing Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 67

Pages Range: 5340-5351

Article Number: 8811624

Journal Issue: 20

DOI: 10.1109/TSP.2019.2937286

Abstract

Feedback delay networks (FDNs) belong to a general class of recursive filters which are widely used in sound synthesis and physical modeling applications. We present a numerical technique to compute the modal decomposition of the FDN transfer function. The proposed pole finding algorithm is based on the Ehrlich-Aberth iteration for matrix polynomials and has improved computational performance of up to three orders of magnitude compared to a scalar polynomial root finder. The computational performance is further improved by bounds on the pole location and an approximate iteration step. We demonstrate how explicit knowledge of the FDN's modal behavior facilitates analysis and improvements for artificial reverberation. The statistical distribution of mode frequency and residue magnitudes demonstrate that relatively few modes contribute a large portion of impulse response energy.

Authors with CRIS profile

Sebastian Schlecht Professur für wahrnehmungsbasierte räumliche Audiosignalverarbeitung (AudioLabs) (Stiftungsprofessur) Emanuël Habets Professur für wahrnehmungsbasierte räumliche Audiosignalverarbeitung (AudioLabs) (Stiftungsprofessur)

How to cite

APA:

Schlecht, S., & Habets, E. (2019). Modal Decomposition of Feedback Delay Networks. IEEE Transactions on Signal Processing, 67(20), 5340-5351. https://doi.org/10.1109/TSP.2019.2937286

MLA:

Schlecht, Sebastian, and Emanuël Habets. "Modal Decomposition of Feedback Delay Networks." IEEE Transactions on Signal Processing 67.20 (2019): 5340-5351.

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