Parameter Tuning of Time-Frequency Masking Algorithms for Reverberant Artifact Removal within the Cochlear Implant Stimulus
This addresses speech perception challenges for cochlear implant users in reverberant conditions, but appears incremental as it focuses on parameter tuning of existing oracle masking methods.
This work tackled the problem of improving speech intelligibility for cochlear implant users in reverberant environments by evaluating different oracle time-frequency masking strategies and their parameterizations. The result involved assessing which techniques led to the best improvements using online speech intelligibility testing with normal-hearing individuals and vocoding, though no concrete numbers were provided in the 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.