Abstract
Machine learning in room acoustics is an emerging field with great potential yet to be explored. Previous research includes predicting acoustic properties such as the impulse response from visual features that include an image of a room. However, one application that can significantly transform the architectural acoustics design process is reconstructing the room geometry based on its acoustic properties. This paper explores applying machine learning in predicting room geometry from an impulse response. The research aims to provide a tool for architects to design a room based on the desired acoustic experience.
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