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

Hanyu Meng; Jeroen Breebaart; Jeremy Stoddard; Vidhyasaharan Sethu; Eliathamby Ambikairajah

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arXiv · 2411.03172

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

Hanyu Meng·Jeroen Breebaart·Jeremy Stoddard·Vidhyasaharan Sethu·Eliathamby Ambikairajah

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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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BibTeX RIS

Hanyu Meng, Jeroen Breebaart, Jeremy Stoddard, Vidhyasaharan Sethu, Eliathamby Ambikairajah. 2024-11-05. Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features. https://arxiv.org/abs/2411.03172

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