Room Acoustical Parameter Estimation from Room Impulse Responses Using Deep Neural Networks

Journal article (2021)

Authors

W. Yu Signal Processing Systems - EEMCS
W.B. Kleijn Victoria University of Wellington, Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2021 W. Yu, W.B. Kleijn
DOI
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https://doi.org/10.1109/TASLP.2020.3043115
Reflection coefficient Room impulse response Room geometry Deep neural network
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

We describe a new method to estimate the geometry of a room and reflection coefficients given room impulse responses. The method utilizes convolutional neural networks to estimate the room geometry and multilayer perceptrons to estimate the reflection coefficients. The mean square error is used as the loss function. In contrast to existing methods, we do not require the knowledge of the relative positions of sources and receivers in the room. The method can be used with only a single RIR between one source and one receiver. For simulated environments, the proposed estimation method can achieve an average of 0.04 m accuracy for each dimension in room geometry estimation and 0.09 accuracy in reflection coefficients. For real-world environments, the room geometry estimation method achieves an accuracy of an average of 0.065 m for each dimension.