AES Show 2024 NY

The utilization of room impulse responses has proven valuable for both the acoustic assessment of indoor environments and music production. Various techniques have been devised over time to capture these responses. Although algorithmic solutions have been in existence since the 1960s for generating synthetic reverberation in real time, they continue to be computationally demanding and in general lack the accuracy in comparison to measured authentic Room Impulse Responses (RIR). In recent times, machine learning has found application in diverse fields, including acoustics, leading to the development of techniques for generating RIRs. This paper provides a general overview, of approaches and methods for generating RIRs, categorized into algorithmic and machine learning techniques, with a particular emphasis on the latter. Discussion covers the acoustical attributes of rooms relevant to perceptual testing and methodologies for comparing RIRs. An examination of disparities between captured and generated RIRs is included to better delineate the key acoustic properties characterizing a room. The paper is designed to offer a general overview for those interested in RIR generation for music production purposes, with future work considerations also explored.