  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.
