Dense Reverberation with Delay Feedback Matrices

Sebastian J Schlecht; Emanuël A P Habets

doi:10.1109/WASPAA.2019.8937284

Dense Reverberation with Delay Feedback Matrices

Sebastian J Schlecht, Emanuël A P Habets

Not published at Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

This paper received the [Best Paper Award](http://waspaa.com/).
Feedback delay networks (FDNs) belong to a general class of recursive filters which are widely used in artificial reverberation and decorrelation applications. One central challenge in the design of FDNs is the generation of sufficient echo density in the impulse response without compromising the computational efficiency. In a previous contribution, we have demonstrated that the echo density of an FDN grows polynomially over time, and that the growth depends on the number and lengths of the delays. In this work, we introduce so-called delay feedback matrices (DFMs) where each matrix entry is a scalar gain and a delay. While the computational complexity of DFMs is similar to a scalar-only feedback matrix, we show that the echo density grows significantly faster over time, however, at the cost of non-uniform modal decays.

Original language	English
Title of host publication	IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
Publisher	IEEE
Pages	1 - 5
DOIs	https://doi.org/10.1109/WASPAA.2019.8937284
Publication status	Published - 2019
MoE publication type	A4 Article in a conference publication
Event	IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - New York, United States Duration: 20 Oct 2019 → 23 Oct 2019 Conference number: 23 https://www.waspaa.com/

Workshop

Workshop	IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Abbreviated title	WASPAA
Country	United States
City	New York
Period	20/10/2019 → 23/10/2019
Internet address	https://www.waspaa.com/

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10.1109/WASPAA.2019.8937284

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Best Paper Award at WASPAA 2019
Schlecht, Sebastian (Recipient), Oct 2019
Prize: Award or honor granted for a specific work

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title = "Dense Reverberation with Delay Feedback Matrices",

abstract = "This paper received the [Best Paper Award](http://waspaa.com/). Feedback delay networks (FDNs) belong to a general class of recursive filters which are widely used in artificial reverberation and decorrelation applications. One central challenge in the design of FDNs is the generation of sufficient echo density in the impulse response without compromising the computational efficiency. In a previous contribution, we have demonstrated that the echo density of an FDN grows polynomially over time, and that the growth depends on the number and lengths of the delays. In this work, we introduce so-called delay feedback matrices (DFMs) where each matrix entry is a scalar gain and a delay. While the computational complexity of DFMs is similar to a scalar-only feedback matrix, we show that the echo density grows significantly faster over time, however, at the cost of non-uniform modal decays.",

author = "Schlecht, {Sebastian J} and Habets, {Emanu{\"e}l A P}",

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Y1 - 2019

N2 - This paper received the [Best Paper Award](http://waspaa.com/). Feedback delay networks (FDNs) belong to a general class of recursive filters which are widely used in artificial reverberation and decorrelation applications. One central challenge in the design of FDNs is the generation of sufficient echo density in the impulse response without compromising the computational efficiency. In a previous contribution, we have demonstrated that the echo density of an FDN grows polynomially over time, and that the growth depends on the number and lengths of the delays. In this work, we introduce so-called delay feedback matrices (DFMs) where each matrix entry is a scalar gain and a delay. While the computational complexity of DFMs is similar to a scalar-only feedback matrix, we show that the echo density grows significantly faster over time, however, at the cost of non-uniform modal decays.

AB - This paper received the [Best Paper Award](http://waspaa.com/). Feedback delay networks (FDNs) belong to a general class of recursive filters which are widely used in artificial reverberation and decorrelation applications. One central challenge in the design of FDNs is the generation of sufficient echo density in the impulse response without compromising the computational efficiency. In a previous contribution, we have demonstrated that the echo density of an FDN grows polynomially over time, and that the growth depends on the number and lengths of the delays. In this work, we introduce so-called delay feedback matrices (DFMs) where each matrix entry is a scalar gain and a delay. While the computational complexity of DFMs is similar to a scalar-only feedback matrix, we show that the echo density grows significantly faster over time, however, at the cost of non-uniform modal decays.

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DO - 10.1109/WASPAA.2019.8937284

M3 - Conference contribution

SP - 1

EP - 5

BT - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

PB - IEEE

T2 - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Y2 - 20 October 2019 through 23 October 2019

ER -

Dense Reverberation with Delay Feedback Matrices

Abstract

Workshop

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Best Paper Award at WASPAA 2019

Cite this