Fig. 1: Given a natural sound in a real-world room that is recorded using a cellphone microphone (left), we estimate the acoustic material properties and the frequency equalization of the room using a novel deep learning approach (middle). We use the estimated acoustic material properties for generating plausible sound effects in the virtual model of the room (right). Our approach is general and robust, and works well with commodity devices.Abstract-We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sources with virtual models. Given the captured audio and an approximate geometric model of a real-world room, we present a novel learning-based method to estimate its acoustic material properties. Our approach is based on deep neural networks that estimate the reverberation time and equalization of the room from recorded audio. These estimates are used to compute material properties related to room reverberation using a novel material optimization objective. We use the estimated acoustic material characteristics for audio rendering using interactive geometric sound propagation and highlight the performance on many real-world scenarios. We also perform a user study to evaluate the perceptual similarity between the recorded sounds and our rendered audio.