Commercial Readiness of a CI NR algorithm

PROJECT SUMMARY / ABSTRACT Cochlear implant (CI) users are typically able to maintain conversations in quiet environments. However, when multiple people are talking simultaneously, such as at a large family dinner or in a restaurant, CI users have great difficulty participating in conversations and frequently withdraw or avoid the situation. Ideally, CI algorithms to remove background talkers (“babble”) from the signal will allow for improved comprehension and conversational engagement. Although CIs incorporate noise reduction (NR) algorithms, these algorithms are not effective when the background is babble. Separating babble from a foreground talker poses two significant challenges. First, the spectral properties of the signal and noise are extremely similar as both are speech. Second, the spectral and temporal properties of multi-talker babble change with time and are therefore difficult to predict. Despite these challenges, we developed an extremely effective algorithm called SEDA to remove babble. SEDA improved understanding of speech in babble at all signal-to-noise ratios (SNRs) tested by an average of 26 percentage points (or 38 points, when normalized with respect to hearing in quiet). In contrast, a commercial NR algorithm (ClearVoice from Advanced Bionics) provided little to no detectable benefit. In a successful Phase 2, we produced a commercially viable implementation of SEDA. Nevertheless, significant work is required to bring SEDA to commercial readiness. The Aims below were developed in conjunction with CI manufacturers to facilitate SEDA technology for licensing by CI manufacturers. Aim 1: Evaluate SEDA in non-babble listening situations. At minimum, SEDA must be beneficial with babble and not detrimental in other listening situations if it is to be commercially implemented into a CI. Therefore, we will evaluate the effect of SEDA in non-babble auditory scenes using speech recognition, listener preference, and a computational metric. Aim 2: Interrogate benefits of SEDA relative to commercial offerings from CI manufacturers. We will compare the effectiveness of SEDA with NR from Advanced Bionics, MED-EL, Cochlear, and Oticon Medical on understating speech in babble, white, and speech-shaped noise. Aim 3: Obtain real-world feedback from at home evaluations of SEDA. We will send patients home for a month with SEDA to collect feedback and to ascertain unexpected issues or listening situations to be addressed. Aim 4: Quantify the effects of computational trade-offs on SEDA performance. We will modify the number of parameters used in SEDA to adjust the computational requirements. Using a computational metric and speech recognition, we will evaluate the effects of the of these changes on SEDA’s performance.