Data-driven modeling of consumer product satisfaction using an orthogonal arrangement of sound attributes

Sound quality attributes have been proven to be important predictors of customer satisfaction with consumer appliances. The results of several studies indicate that level-based, tonal, and temporal aspects of sound influence the perceived quality of consumer products. The current state-of-the-art of inferring consumer satisfaction with sound attributes is based on the jury test methodology. However, listening-based jury studies fail to capture interaction effects nor study sound samples based on an orthogonal arrangement of sound quality attributes. Therefore, this study aims to develop data-driven models to mathematically relate sound attributes to consumer satisfaction. A baseline sound obtained from the operational noise generated by a fan-powered home appliance was used as a reference sound for researching the importance of sound quality attributes. An orthogonal-designed experiment was conducted to prepare sound samples based on selected sound attributes, including loudness, sharpness, roughness, and prominence ratio. Consumer studies were conducted to measure the product satisfaction of the various sound samples, which were used to train the data-driven model. The data-driven model was shown to accurately predict consumer satisfaction metrics based on sound quality attributes. Therefore, the proposed methods are valuable for evaluating new products within the model inference space without conducting further listening-based jury studies.