Applications of Static and Dynamic Iterated Rippled Noise to Evaluate Pitch Encoding in the Human Auditory Brainstem

This paper presents a new application of the dynamic iterated rippled noise (IRN) algorithm by generating dynamic pitch contours representative of those that occur in natural speech in the context of EEG and the frequency following response (FFR). Besides IRN steady state and linear rising stimuli, curvilinear rising stimuli were modeled after pitch contours of natural productions of Mandarin Tone 2. Electrophysiological data on pitch representation at the level of the brainstem, as reflected in FFR, were evaluated for all stimuli, static or dynamic. Autocorrelation peaks were observed corresponding to the fundamental period (tau) as well as spectral bands at the fundamental and its harmonics for both a low and a high iteration step. At the higher iteration step, both spectral and temporal FFR representations were more robust, indicating that both acoustic properties may be utilized for pitch extraction at the level of the brainstem. By applying curvilinear IRN stimuli to elicit FFRs, we can evaluate the effects of temporal degradation on 1) the neural representation of linguistically-relevant pitch features in a target population (e.g. cochlear implant) and 2) the efficacy of signal processing schemes in conventional hearing aids and cochlear implants to recover these features.