Identifying the acoustic fingerprints of trigger sounds and predicting discomfort for misophonia

Human hearing is critical to everyday communication and the perception of natural auditory scenes. For individuals with misophonia, sounds commonly experienced in daily life can evoke severe discomfort and distress. Aversion is often described in terms of broad sound categories, such as bodily sounds, but what acoustic features cause specific sounds to be aversive or not, within the same category or across different individuals, remains unclear. Here, we explore whether bottom-up statistical sound features processed in the auditory periphery and midbrain can explain aversion to sounds. Using the Free Open-Access Misophonia Stimuli (FOAMS) dataset and a hierarchical model of the auditory system, we find that sound summary statistics can predict discomfort ratings in participants with misophonia. For each listener, the model produces individualized transfer functions that pinpoint the specific spectrotemporal modulations that contribute towards sound aversion. Overall, the model explains 76% of the variance in discomfort ratings, and we find substantial differences across participants in which sound features drive aversion. A major advantage of the modeling approach here is that it is sound-computable - perceptual ratings can be fit from or predicted for any sound. To illustrate applications of sound-computable models, we consider 1) extrapolation of participants' ratings to a large set of untested environmental sounds and develop 2) personalized trigger detection that uses the listener's acoustic feature preferences to identify potential triggers in continuous audio. Model predictions identify many untested sound categories, not in the original FOAMS set, that may also be aversive and suggest that there may be substantial heterogeneity in how aversive specific sounds are within some sound categories. In continuous audio, we show how sound-computable models can identify the timing of potential triggers from sound mixtures. Altogether, our results suggest that acoustic features - spectrotemporal modulations, in particular - can practically be used to characterize the individualized patterns of aversion in participants with misophonia. Future perceptual studies using synthetic sounds and sound sets with more diverse acoustics will allow model predictions to be tested more broadly; however, sound-computable models may already have applications in precision diagnosis and management of misophonia.