Room Geometry Estimation from Room Impulse Responses using Convolutional Neural Networks

2019/04/01 by Wangyang Yu, Yu, Wangyang, W. Bastiaan Kleijn +1 · 1 citation
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Audio and Speech Processing (eess.AS) #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1904.00869

openalex publication_date 2019/04/01 · openalex created_date 2019/04/11 · openalex updated_date 2026/07/28

Abstract

We describe a new method to estimate the geometry of a room given room impulse responses. The method utilises convolutional neural networks to estimate the room geometry and uses the mean square error as the loss function. In contrast to existing methods, we do not require the position or distance of sources or receivers in the room. The method can be used with only a single room impulse response between one source and one receiver for room geometry estimation. The proposed estimation method can achieve an average of six centimetre accuracy. In addition, the proposed method is shown to be computationally efficient compared to state-of-the-art methods.

Citations

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