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

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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.

Original languageEnglish
Article number9286412
Pages (from-to)436 - 447
Number of pages12
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume29
DOIs
Publication statusPublished - 2021

Keywords

  • deep neural network
  • reflection coefficient
  • room geometry
  • Room impulse response

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