An Investigation of the Effect of AGC Gain on the Output Signal to Noise Ratio in Cochlear Implant Sound Processing

Measurement of speech intelligibility of cochlear implant (CI) recipients is typically carried out with a speech-innoise test procedure. Metrics which predict speech intelligibility can pre-screen new sound processing strategies prior to comprehensive testing with human subjects. The Output Signal to Noise Ratio (OSNR) metric calculates the Signal to Noise Ratio (SNR) which is present at the CI sound processor output. Watkins et al. (2018) found OSNR was an accurate predictor of speech intelligibility that could predict intelligibility in scenarios where other predictors could not. The current study investigated the effect of the sound processor automatic gain control (AGC) on OSNR and a simplified metric, Separate gain SNR (SSNR), which calculated the SNR at the CI output, assuming no interaction between the signal and noise in the sound processor. Prediction accuracy of OSNR was compared to that of Input SNR and SSNR. It was found that AGC-induced distortion and SNR degradation in speech gaps worsened OSNR. For scenarios with significant non-linear, time-varying processing, OSNR was the most accurate prediction metric. SSNR was found to be an inaccurate predictor.