Toward an adaptive procedure for multi-frequency categorical loudness scaling: A Monte Carlo simulation study

Abstract

To capture a listener’s loudness perception profile, categorical loudness scaling (CLS) is typically repeated at various frequencies. The current study aims to develop psychophysical procedures that enable simultaneous estimation of loudness growth across frequencies. For these procedures, the listener hears a pure-tone stimulus provides a categorical rating (“Soft,” “Loud,” etc.) on each trial. After a response is collected, the procedures update a model of the loudness profile and leverage the model to optimize the stimulus (i.e., level and frequency) for the next trial. The modified slope-adaptive procedure selects the stimulus from a uniform distribution spanning the model-predicted dynamic range, while the modified maximum expected information (MEI) procedure optimizes the stimulus based on an entropy metric. Monte Carlo simulations were conducted to evaluate the two procedures using a database that consists of CLS data collected from 148 listeners at Boys Town National Research Hospital. For each listener, the two procedures were run based on responses simulated using their known loudness profiles (i.e., the ground truth). Both procedures were able to estimate the loudness profile close to the ground truth, with a root-mean-square error (RMSE) of about 6 dB after 100 trials. Below 100 trials, the modified MEI procedure showed a lower RMSE.