Sound quality preference modeling using a nested artificial neural network architecture

Sound quality analysis is a process that is used to study subjective perception of the functionality or preference of a product based on the sound that it makes. Paired comparison jury testing is often used to understand preference on a pairwise basis and map the results onto an interval scale using a Bradley-Terry model. This provides a merit score for each of the sounds used in the study. A statistical model, such as multiple linear regression (MLR), is then used to model the merit score using objective metrics. This statistical model, or preference equation, then allows the results of the jury testing to be generalized for use with other sounds. One drawback of this approach is the necessity to assume the form of the preference prior to applying the statistical model and the requirement of the Bradley-Terry model for a full and balanced jury study. The present paper proposes a method of using a nested artificial neural network (nested ANN) to learn the paired preferences directly. The nested ANN architecture can uncover non-linear preferences and does not require a full and balanced jury study. The nested ANN model is applied to real golf club jury study results to identify the presence of a non-linear preference for the pitch of the ball impact sound. The paper concludes by demonstrating how the nested ANN model can be simplified to ease interpretation of the preference model and provide a means of optimizing the sound quality in the context of active noise control for sound quality.