Wearable devices estimate audio quality and listening effort
Physiology-Informed Audio Quality and Listening Effort Estimation
SoundNeural and Evolutionary Computing
Summary
Audio quality is usually judged by people after hearing sounds, which takes time and can vary a lot. The authors show that wearable brain and eye sensors can measure how good the sound is and how hard it is to listen to for each person. They compared lab equipment with consumer devices and found that cheaper, wearable tools still give useful information. This could help make sound systems that adapt to each listener's experience in real time.
What this means in practice
- •For audio system developers: Use wearable sensors to adapt audio playback based on real-time estimates of user listening effort and quality perception.
- •For hearing aid designers: Incorporate physiological feedback from wearable devices to personalize hearing aid settings considering individual listening effort.
Authors
Niall McGuire, Dimitra Emmanouilidou
Abstract
Audio systems aim to maximise the listener's perceived quality, traditionally evaluated through mean opinion scoring (MOS), a subjective process that is slow, costly, and collected only after playback. Machine learning now predicts MOS directly from the audio, but in generalising across participants it tends to discard the subject variance that defines individual perception. Neurophysiological measures may offer a complementary signal taken directly from the listener, objective, implicit, and continuous, though prior work has relied on laboratory-grade EEG and fNIRS. We ask how practical consumer-grade devices compare, and whether they might recover population-level audio-quality trends and, when calibrated per listener, track that listener's effort. Recording 32-channel laboratory EEG alongside wearable EEG, fNIRS, and eye tracking from thirty listeners, our results suggest commercial wearable and optical sensing can decode audio quality at statistically significant levels and recover listening effort per listener. These findings may point towards platforms tuned not only to perceived quality but to the effort a clip demands, read from the listener's own response as they listen.