Abstract The operational noise of electronic expansion valve (EEV) is the main component of noise source for the indoor unit of the air conditioner. The sound quality of EEV noise directly influences the consumer’s perceptions. Based on deep belief network (DBN) technique, an objective model has been conducted to evaluate the sound quality of EEV. Gaussian Restricted Boltzmann Machines (GRBM) provides the capability of modeling continuous data and has been used to develop a DBN model in this paper. The interior noise of 46 EEVs under working conditions were measured and corresponding subjective evaluation were implemented. A linear regression-based DBN (LR-DBN) is proposed with 6 psychoacoustic metrics and 26 Mel-frequency cepstral coefficients (MFCC) as input features. The performance of LR-DBN was validated against an ordinary DBN, a multiple linear regression (MLR) and a back-propagation neural network (BPNN). The results show that the LR-DBN has higher correlation coefficient and lower prediction error with human perception compared to the other considered methods. In addition, LR-DBN shows better stability than the other models. This present method may be a reliable approach for evaluating EEV sound.

中文翻译：

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基于DBN的高斯受限玻尔兹曼机电子膨胀阀声品质评价

摘要 电子膨胀阀（EEV）的运行噪声是空调室内机噪声源的主要组成部分。EEV噪声的音质直接影响消费者的感知。基于深度置信网络（DBN）技术，建立了一个客观模型来评估 EEV 的声音质量。Gaussian Restricted Boltzmann Machines (GRBM) 提供了对连续数据进行建模的能力，并已在本文中用于开发 DBN 模型。对46辆电动汽车在工况下的车内噪声进行了测量，并进行了相应的主观评价。提出了一种基于线性回归的 DBN (LR-DBN)，它具有 6 个心理声学指标和 26 个 Mel 频率倒谱系数 (MFCC) 作为输入特征。LR-DBN 的性能已针对普通 DBN 进行了验证，多元线性回归 (MLR) 和反向传播神经网络 (BPNN)。结果表明，与其他考虑的方法相比，LR-DBN 具有更高的相关系数和更低的人类感知预测误差。此外，LR-DBN 表现出比其他模型更好的稳定性。本方法可能是用于评估 EEV 声音的可靠方法。