Modeling the effects of dynamic range compression on signals in noise

TL;DR

This paper develops a mathematical model to analyze how dynamic range compression affects signals in noisy environments, revealing its distortion effects and impact on signal-to-noise ratio in hearing aids.

Contribution

It introduces a novel mathematical framework for studying DRC in noise, providing theoretical insights into its distortion and effectiveness.

Findings

Uncorrelated signal envelopes become negatively correlated after DRC.

Effective compression is weaker in mixtures than for individual signals.

DRC can reduce long-term signal-to-noise ratio under certain conditions.

Abstract

Hearing aids use dynamic range compression (DRC), a form of automatic gain control, to make quiet sounds louder and loud sounds quieter. Compression can improve listening comfort, but it can also cause distortion in noisy environments. It has been widely reported that DRC performs poorly in noise, but there has been little mathematical analysis of these distortion effects. This work introduces a mathematical model to study the behavior of DRC in noise. Using statistical assumptions about the signal envelopes, we define an effective compression function that models the compression applied to one signal in the presence of another. This framework is used to prove results about DRC that have been previously observed experimentally: that when DRC is applied to a mixture of signals, uncorrelated signal envelopes become negatively correlated; that the effective compression applied to each sound in a mixture is weaker than it would have been for the signal alone; and that compression can reduce the long-term signal-to-noise ratio in certain conditions. These theoretical results are supported by software experiments using recorded speech signals.