An Audio-Visual Method for Room Boundary Estimation and Material Recognition

In applications such as virtual and augmented reality, a plausible and coherent audio-visual reproduction can be achieved by deeply understanding the reference scene acoustics. This requires knowledge of the scene geometry and related materials. In this paper, we present an audio-visual approach for acoustic scene understanding. We propose a novel material recognition algorithm, that exploits information carried by acoustic signals. The acoustic absorption coefficients are selected as features. The training dataset was constructed by combining information available in the literature, and additional labeled data that we recorded in a small room having short reverberation time (RT60). Classic machine learning methods are used to validate the model, by employing data recorded in five rooms, having different sizes and RT60s. The estimated materials are utilized to label room boundaries, reconstructed by a vision-based method. Results show 89% and 80% agreement between the estimated and reference room volumes and materials, respectively.