Situational Signal Processing with Ecological Momentary Assessment: Advancing Speech Vocoder Implementation for Naturalistic Cochlear Implant Scenarios

Taylor Lawson, John H. L. Hansen

Confirmation: I have read and agree with the IEEE BSN 2025 conference submission's policy on behalf of myself and my co-authors.

Keywords: cochlear implant (CI), situational signal processing, wearable and portable devices, GET Vocoder, sound source localization, non-linguistic, CCi-MOBILE, “Emaging”

TL;DR: “Emaging,” a CI framework using the GET Vocoder, enables real-time sound identification and localization through wearable ecological momentary assessment (EMA) on portable devices in natural listening environments.

Abstract: Cochlear implants (CIs) are surgically implanted medical devices that rely on real-time digital signal processing (DSP) strategies for acoustic-to-sound conversion. Because most fixed strategies have been implemented and tested only in clinical and laboratory settings, the ability for CI systems to adapt to varied feedback in spontaneous environments is limited. To help allocate real-time CI feedback in naturalistic spaces, this study proposes the first CI framework for situational signal processing: “Emaging”, and considers CI vocoded testing approaches to help record and document collected data when CI users are often difficult to recruit for experimental testing. This unprecedented application implements ecological momentary assessment (EMA), an “on-the-go” data collection method for instantaneous feedback from CI subjects. The “Emaging” algorithm solution runs on portable devices alongside CCi-MOBILE, a customized portable CI signal processing platform. This study evaluates two parameters of EMA for the CI participant: sound source localization (SSL) and sound source identification (SSI) for non-spoken sounds. With “Emaging”, CI users document and “tag” situational data from their naturalistic environments in real-time. Due to the many constraints with CI subject recruitment and testing, vocoded simulations with normal hearing (NH) participants can contribute valuable information and considerations aptly integrated with CI algorithm development. “Emaging” and its collected responses from CI, NH, and vocoded (V) subjects provides a unique opportunity for next generational CI processing design that integrates effective sound coding strategies for non-linguistic sound intelligibility and source localization.

Track: 12. Emerging Topics (e.g. Agentic AI, LLMs for computational health with wearables)

NominateReviewer: Taylor Lawson [e-mail]

Submission Number: 88