Evaluation of two algorithms for detecting human frequency-following responses to voice pitch

Abstract

The automated procedure used in this study, including the use of the control-experimental protocol and response thresholds used for each of the five objective indices, can be used for difficult-to-test patients and may prove to be useful as an assessment and diagnostic method in both clinical and basic research efforts.

Exponential modeling of human frequency-following responses to voice pitch

“…periodicity) of a sampled signal. One is to measure the strength of the overall periodicity of a sampled signal in the temporal domain by using an autocorrelation algorithm (Krishnan et al, 2005;Wong et al, 2007;Jeng et al, 2011). Briefl y, this method employs an autocorrelation function that multiplies a sampled signal with a time-shifted copy of itself.…”

“…Strength of the overall periodicity of the sampled signal is then determined by calculating the peak-to-trough amplitude within a certain range of time shifts in the normalized autocorrelation output. The other approach is to examine how accurately the spectral energy of a response follows the fundamental frequency ( f0 ) contour of the stimulus by using a narrow-band spectrogram algorithm (Russo et al, 2008;Song et al, 2008;Jeng et al, 2011). Briefl y, this algorithm analyses the spectral components of an incoming signal by using a slidingwindow technique.…”

Evaluation of an Automated Procedure for Detecting Frequency-Following Responses in American and Chinese Neonates

“…If the response is meant to be an objective method to examine the mechanisms of pitch processing in the human brain stem, development and validation of an automated procedure suitable for detecting the presence of such a response is needed. To move toward that optimal goal, an automated procedure has been proposed and examined (Jeng, Hu, Dickman, Lin, Lin, Wang, et al, 2011). Specifi cally, in that study, a control-experimental protocol and response-threshold criteria were utilized to detect the presence of a response in Chinese adult participants.…”

Using frequency-following responses (FFRs) to evaluate the auditory function of frequency-modulation (FM) discrimination

“…Pitch strength of each frame was calculated by finding the longitudinal distance between the first peak and the subsequent trough in the autocorrelation function output (Jeng et al 2011). Because the f 0 contours of all stimuli used in this study fell within the…”

“…However, it was suggested that the neural processing of repetitive FM sweeps in the human auditory cortex differs from that of pure tones (Okamoto and Kakigi 2017). Tonal sweeps and syllables were used to evoke FFRs to test the effect of signal phase on neural encoding of speechlike sounds (Jeng et al 2011;Bidelman 2014). These studies mainly focused on developing new metrics for analysis of FFRs, or on factors impacting the neural representation of speechlike sounds.…”