Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features

• URL: http://arxiv.org/abs/2411.03172v1
• Date: Tue, 05 Nov 2024 15:20:23 GMT
• Title: Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features
• Authors: Hanyu Meng, Jeroen Breebaart, Jeremy Stoddard, Vidhyasaharan Sethu, Eliathamby Ambikairajah,
• Abstract summary: We propose a unified framework that blindly estimates reverberation time (T60), direct-to-reverberant ratio (DRR) and clarity (C50) across 10 frequency bands. The proposed framework utilizes a novel feature named Spectro-Spatial Co Vector (SSCV), efficiently representing temporal, spectral as well as spatial information of the FOA signal.
• Score: 10.480691005356967
• License:
• Abstract: Estimating frequency-varying acoustic parameters is essential for enhancing immersive perception in realistic spatial audio creation. In this paper, we propose a unified framework that blindly estimates reverberation time (T60), direct-to-reverberant ratio (DRR), and clarity (C50) across 10 frequency bands using first-order Ambisonics (FOA) speech recordings as inputs. The proposed framework utilizes a novel feature named Spectro-Spatial Covariance Vector (SSCV), efficiently representing temporal, spectral as well as spatial information of the FOA signal. Our models significantly outperform existing single-channel methods with only spectral information, reducing estimation errors by more than half for all three acoustic parameters. Additionally, we introduce FOA-Conv3D, a novel back-end network for effectively utilising the SSCV feature with a 3D convolutional encoder. FOA-Conv3D outperforms the convolutional neural network (CNN) and recurrent convolutional neural network (CRNN) backends, achieving lower estimation errors and accounting for a higher proportion of variance (PoV) for all 3 acoustic parameters.

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