One-Shot Acoustic Matching Of Audio Signals -- Learning to Hear Music In Any Room/ Concert Hall

TL;DR

This paper introduces a neural network architecture capable of transforming sounds to match the acoustic signature of any room or hall, enabling realistic auralizations from arbitrary audio recordings.

Contribution

It presents a novel neural network model that learns residual signals to adapt sounds to different acoustic spaces using simple signal processing principles.

Findings

Achieves realistic sound transformations in various acoustic environments.

Outperforms traditional impulse response methods in flexibility and quality.

Provides a framework for one-shot acoustic matching using arbitrary audio samples.

Abstract

The acoustic space in which a sound is created and heard plays an essential role in how that sound is perceived by affording a unique sense of \textit{presence}. Every sound we hear results from successive convolution operations intrinsic to the sound source and external factors such as microphone characteristics and room impulse responses. Typically, researchers use an excitation such as a pistol shot or balloon pop as an impulse signal with which an auralization can be created. The room "impulse" responses convolved with the signal of interest can transform the input sound into the sound played in the acoustic space of interest. Here we propose a novel architecture that can transform a sound of interest into any other acoustic space(room or hall) of interest by using arbitrary audio recorded as a proxy for a balloon pop. The architecture is grounded in simple signal processing ideas to learn residual signals from a learned acoustic signature and the input signal. Our framework allows a neural network to adjust gains of every point in the time-frequency representation, giving sound qualitative and quantitative results.