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Intelligent systems approaches to product sound quality evaluations - A review

机译：产品音质评估的智能系统方法-评论

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摘要

As a product market becomes more competitive, consumers become more discriminating in the way in which they differentiate between engineered products. The consumer often makes a purchasing decision based on the sound emitted from the product during operation, by using the sound to judge quality or annoyance. Therefore, in recent years, many sound quality analysis tools have been developed to evaluate the consumer preference as it relates to a product sound and to quantify this preference based on objective measurements. This understanding can be used to direct a product design process in order to help differentiate the product from competitive products or to establish an impression on consumers regarding a product's quality or robustness. The sound quality process is typically a statistical tool that is used to model subjective preference, or merit score, based on objective measurements, or metrics. In this way, new product developments can be evaluated in an objective manner without the laborious process of gathering a sample population of consumers for subjective studies each time. The most common model used today is the Multiple Linear Regression (MLR), although recently non-linear Artificial Neural Network (ANN) approaches are gaining popularity. To shed further light into these approaches, this paper is written to review the sound quality process and neural network models, and extend these introductions into a discussion regarding the value that can be gained in using an intelligent systems approach, namely ANNs, to sound quality analysis. The paper goes into specific shortcomings that are associated with both the current regression and neural network approaches, and concludes with new thoughts regarding a robust approach to improving the current state-of-the-art technology.
机译：随着产品市场竞争的加剧，消费者在区分工程产品的方式上将越来越有区别。消费者通常根据产品在操作过程中发出的声音，通过使用声音来判断质量或烦恼来做出购买决定。因此，近年来，已经开发了许多声音质量分析工具以评估与产品声音相关的消费者偏好，并基于客观测量来量化该偏好。这种理解可以用来指导产品设计过程，以帮助将产品与竞争产品区分开来，或在消费者对产品的质量或耐用性方面产生印象。声音质量过程通常是一种统计工具，用于根据客观测量或指标对主观偏好或功绩评分进行建模。以此方式，可以客观地评估新产品的开发，而无需费时费力地收集样本的消费者样本进行主观研究。今天使用的最常见模型是多元线性回归（MLR），尽管最近非线性人工神经网络（ANN）方法越来越受欢迎。为了进一步阐明这些方法，本文旨在审查声音质量过程和神经网络模型，并将这些介绍扩展到有关使用智能系统方法（即人工神经网络）可以为声音质量带来的价值的讨论中。分析。本文探讨了与当前回归方法和神经网络方法都相关的特定缺点，并以关于改进当前最新技术的健壮方法的新思路作了总结。

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  • 来源
    《Applied Acoustics》 |2012年第10期|p.987-1002|共16页
  • 作者

    Glenn Pietila; Teik C. Lim;

  • 作者单位

    Vibro-Acoustics and Sound Quality Research Laboratory, School of Dynamic Systems, College of Engineering and Applied Science,Mechanical Engineering, 598 Rhodes Hall, P.O. Box 210072, University of Cincinnati, OH 45221-0072, USA;

    Vibro-Acoustics and Sound Quality Research Laboratory, School of Dynamic Systems, College of Engineering and Applied Science,Mechanical Engineering, 598 Rhodes Hall, P.O. Box 210072, University of Cincinnati, OH 45221-0072, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    intelligent systems; product sound quality; multiple linear regression; artificial neural network;

    机译：智能系统;产品音质;多元线性回归;人工神经网络;

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