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Modeling room acoustics in a field setting involves some degree of blindparameter estimation from noisy and reverberant audio. Modern approachesleverage convolutional neural networks (CNNs) in tandem with time-frequencyrepresentation. Using short-time Fourier transforms to develop thesespectrogram-like features has shown promising results, but this methodimplicitly discards a significant amount of audio information in the phasedomain. Inspired by recent works in speech enhancement, we propose utilizingnovel phase-related features to extend recent approaches to blindly estimatethe so-called "reverberation fingerprint" parameters, namely, volume and RT60.The addition of these features is shown to outperform existing methods thatrely solely on magnitude-based spectral features across a wide range ofacoustics spaces. We evaluate the effectiveness of the deployment of thesenovel features in both single-parameter and multi-parameter estimationstrategies, using a novel dataset that consists of publicly available roomimpulse responses (RIRs), synthesized RIRs, and in-house measurements of realacoustic spaces.

Blind Acoustic Room Parameter Estimation Using Phase Features