Blind Acoustic Room Parameter Estimation Using Phase Features

• Main Authors: Ick, Christopher, Mehrabi, Adib, Jin, Wenyu
• Format: Journal Article
• Language: English
• Published: 13-03-2023
• Subjects: Computer Science - Learning; Computer Science - Sound
• DOI: 10.48550/arxiv.2303.07449

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.