Enhancing Audio Perception of Music By AI Picked Room Acoustics
Prateek Verma, Jonathan Berger

TL;DR
This paper uses AI to identify optimal room acoustics for music playback, enhancing perceptual quality by simulating different environments and selecting the best or improving sound quality.
Contribution
It introduces a convolutional AI model that rates perceptual qualities of sounds and applies synthetic room impulse responses to optimize audio perception.
Findings
Achieved 78% accuracy in rating perceptual qualities.
Developed a library of 60,000 synthetic impulse responses.
Demonstrated improved sound quality and optimal room selection.
Abstract
Every sound that we hear is the result of successive convolutional operations (e.g. room acoustics, microphone characteristics, resonant properties of the instrument itself, not to mention characteristics and limitations of the sound reproduction system). In this work we seek to determine the best room in which to perform a particular piece using AI. Additionally, we use room acoustics as a way to enhance the perceptual qualities of a given sound. Historically, rooms (particularly Churches and concert halls) were designed to host and serve specific musical functions. In some cases the architectural acoustical qualities enhanced the music performed there. We try to mimic this, as a first step, by designating room impulse responses that would correlate to producing enhanced sound quality for particular music. A convolutional architecture is first trained to take in an audio sample and…
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Taxonomy
TopicsSpeech and Audio Processing · Music and Audio Processing · Hearing Loss and Rehabilitation
MethodsLib · Convolution
