Blind Acoustic Room Parameter Estimation Using Phase Features

• URL: http://arxiv.org/abs/2303.07449v1
• Date: Mon, 13 Mar 2023 20:05:41 GMT
• Title: Blind Acoustic Room Parameter Estimation Using Phase Features
• Authors: Christopher Ick, Adib Mehrabi, Wenyu Jin
• Abstract summary: We propose utilizing novel phase-related features to extend recent approaches to blindly estimate the so-called "reverberation fingerprint" parameters. The addition of these features is shown to outperform existing methods that rely solely on magnitude-based spectral features.
• Score: 4.473249957074495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling room acoustics in a field setting involves some degree of blind parameter estimation from noisy and reverberant audio. Modern approaches leverage convolutional neural networks (CNNs) in tandem with time-frequency representation. Using short-time Fourier transforms to develop these spectrogram-like features has shown promising results, but this method implicitly discards a significant amount of audio information in the phase domain. Inspired by recent works in speech enhancement, we propose utilizing novel phase-related features to extend recent approaches to blindly estimate the so-called "reverberation fingerprint" parameters, namely, volume and RT60. The addition of these features is shown to outperform existing methods that rely solely on magnitude-based spectral features across a wide range of acoustics spaces. We evaluate the effectiveness of the deployment of these novel features in both single-parameter and multi-parameter estimation strategies, using a novel dataset that consists of publicly available room impulse responses (RIRs), synthesized RIRs, and in-house measurements of real acoustic spaces.

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