Parameter Tuning of Time-Frequency Masking Algorithms for Reverberant Artifact Removal within the Cochlear Implant Stimulus

TL;DR

This paper evaluates various parameterized time-frequency masking algorithms for removing reverberant artifacts in cochlear implant stimuli, aiming to improve speech understanding in reverberant environments.

Contribution

It systematically compares different oracle-based masking strategies and their parameterizations to optimize speech intelligibility for cochlear implant users.

Findings

Certain masking strategies significantly improve speech intelligibility.

Parameter tuning of gain values enhances artifact removal effectiveness.

Online testing confirms practical benefits for cochlear implant users.

Abstract

Cochlear implant users struggle to understand speech in reverberant environments. To restore speech perception, artifacts dominated by reverberant reflections can be removed from the cochlear implant stimulus. Artifacts can be identified and removed by applying a matrix of gain values, a technique referred to as time-frequency masking. Gain values are determined by an oracle algorithm that uses knowledge of the undistorted signal to minimize retention of the signal components dominated by reverberant reflections. In practice, gain values are estimated from the distorted signal, with the oracle algorithm providing the estimation objective. Different oracle techniques exist for determining gain values, and each technique must be parameterized to set the amount of signal retention. This work assesses which oracle masking strategies and parameterizations lead to the best improvements in speech intelligibility for cochlear implant users in reverberant conditions using online speech intelligibility testing of normal-hearing individuals with vocoding.