This paper presents an enhanced artificial neural network (ANN) model for predicting the liking of food using the Harris Hawks optimiser (HHO) in the presence of different masking background noise types and levels, relative to the ambient background noise (i.e. no noise conditions). The results showed that the proposed model can predict the relative liking food ratings with higher performance (R² = 0.70, RMSE = 0.8), as compared to traditional ANNs using feedforward neural networks (FFNNs) (R² = 0.61, RMSE = 1.1) and statistical mixed models (R² = 0.42, RMSE = 1.8). This model was used to find the threshold level that gives maximum relative food liking ratings for different types of noise. This threshold level varied between 30 and 35 dBA for three noise types. The liking of food in the presence of background noise depends on acoustic and non-acoustic factors. A feature analysis using the “RReliefF” algorithm was used to investigate the relative importance of these acoustic and non-acoustic factors on the relative food liking using predicted model outcomes. Acoustic factors such as noise type and level had higher importance weights on the relative liking of food in the presence of background noise than non-acoustic factors such as gender, sensitivity, and age. The results presented herein are relevant for future and more targeted noise assessment and mitigation strategies in dining areas.

本文提出了一种增强的人工神经网络（ANN）模型，用于在相对于环境背景噪声（即无噪声条件）存在不同掩蔽背景噪声类型和水平的情况下，使用哈里斯·霍克斯霍克斯优化器（HHO）来预测食物的喜好。 。结果表明，与 使用前馈神经网络（FFNN）的传统人工神经网络（R ²  = 0.61，RMSE = 1.1）相比，所提出的模型可以预测具有较高性能的相对喜欢的食物等级（R ² = 0.70，RMSE = 0.8）。和统计混合模型（R ² = 0.42，RMSE = 1.8）。该模型用于查找阈值水平，该阈值水平针对不同类型的噪声给出了最大的食物偏爱等级。对于三种噪声类型，此阈值水平在30至35 dBA之间变化。在存在背景噪声的情况下，食物的喜欢程度取决于声学和非声学因素。使用“ RReliefF”算法进行特征分析，使用预测的模型结果调查这些声学和非声学因素对相对食物偏好的相对重要性。与背景，性别，敏感性和年龄等非声学因素相比，诸如噪声类型和水平等声学因素对食品在有背景噪声的情况下相对喜欢的重要性权重更高。本文提供的结果与就餐区域的未来和更有针对性的噪声评估和缓解策略有关。