Preliminary investigation of the short-term in situ performance of an automatic masker selection system

TL;DR

This study evaluates the real-world short-term performance of an automatic masker selection system (AMSS) in an urban park, comparing predicted and subjective pleasantness and assessing different masker schemes' effects on acoustic comfort.

Contribution

It provides the first in situ assessment of an AMSS trained on subjective responses, comparing its predictions to actual user evaluations in a real environment.

Findings

AMSS predictions correlated with subjective pleasantness scores.

Different masker schemes significantly affected perceived acoustic quality.

AMSS outperformed simple and random masker selection methods.

Abstract

Soundscape augmentation or "masking" introduces wanted sounds into the acoustic environment to improve acoustic comfort. Usually, the masker selection and playback strategies are either arbitrary or based on simple rules (e.g. -3 dBA), which may lead to sub-optimal increment or even reduction in acoustic comfort for dynamic acoustic environments. To reduce ambiguity in the selection of maskers, an automatic masker selection system (AMSS) was recently developed. The AMSS uses a deep-learning model trained on a large-scale dataset of subjective responses to maximize the derived ISO pleasantness (ISO 12913-2). Hence, this study investigates the short-term in situ performance of the AMSS implemented in a gazebo in an urban park. Firstly, the predicted ISO pleasantness from the AMSS is evaluated in comparison to the in situ subjective evaluation scores. Secondly, the effect of various masker selection schemes on the perceived affective quality and appropriateness would be evaluated. In total, each participant evaluated 6 conditions: (1) ambient environment with no maskers; (2) AMSS; (3) bird and (4) water masker from prior art; (5) random selection from same pool of maskers used to train the AMSS; and (6) selection of best-performing maskers based on the analysis of the dataset used to train the AMSS.