In every day life, sounds from the environment are crucial in understanding, navigating, and interacting with the world around us. The room impulse response (RIR) is the underlying acoustic signature of a space present in all the real-world sounds we hear: played back by itself it is a time-domain signal that sounds like an echo. A dry RIR indicates that the room is small and/or is surfaced with acoustically dampening materials, whereas a loud ringing RIR indicates the environment is big and/or has hard, reflective surfaces. RIRs are used to add spatiality to dry recorded sound by a convolution operation, and thus are used in many applications such as adding reverb to music, acoustical rendering of architectural spaces, adding spatiality to audio sources in virtual environments, and more. However, in the cases where obtaining an RIR through real-world recording is too resource intensive or impossible, we are faced with the non-trivial task of generating RIRs.
The task of the thesis, RIR Completion, is to synthesize the remainder of the RIR given the early part of the RIR, about 50ms, using machine learning (ML). This approach would reduce the amount of time and compute of by only requiring traditional simulation methods to produce a fraction of the time samples of the RIR, a short RIR "head", as the input to a neural network (NN), which would subsequently generate the rest of the samples, the RIR "tail". with fixed time and compute cost. A trained neural network on this task generates RIRs with fixed time and compute cost, and because it is a data-based approach, can potentially be more accurate than other algorithmic reverbs.