Fault identification using the emitted mechanical noise is becoming an attractive field of research in a variety of industries. It is essential to rank acoustic feature integration functions on their efficiency to classify different types of sound for conducting a fault diagnosis. The Mel frequency cepstral coefficient (MFCC) method was used to obtain various acoustic feature sets in the current study. MFCCs represent the audio signal power spectrum and capture the timbral information of sounds. The objective of this study is to introduce a method for the selection of statistical indicators to integrate the MFCC feature sets. Two purpose-built audio datasets for squeak and rattle were created for the study. Data were collected experimentally to investigate the feature sets of 256 recordings from 8 different rattle classes and 144 recordings from 12 different squeak classes. The support vector machine method was used to evaluate the classifier accuracy with individual feature sets. The outcome of this study shows the best performing statistical feature sets for the squeak and rattle audio datasets. The method discussed in this pilot study is to be adapted to the development of a vehicle faulty sound recognition algorithm.

中文翻译：

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Mel频率倒谱系数时间特征集成，用于分类吱吱声和嘎嘎声

使用发出的机械噪声进行故障识别正在成为各行各业的一个有吸引力的研究领域。必须根据其效率对声学特征集成函数进行排名，以对不同类型的声音进行分类以进行故障诊断。梅尔频率倒谱系数（MFCC）方法用于获得当前研究中的各种声学特征集。MFCC 表示音频信号功率谱并捕获声音的音色信息。本研究的目的是介绍一种选择统计指标的方法来整合 MFCC 特征集。为该研究创建了两个专门用于吱吱声和嘎嘎声的音频数据集。通过实验收集数据以研究来自 8 个不同拨浪鼓类别的 256 个录音和来自 12 个不同吱吱声类别的 144 个录音的特征集。支持向量机方法用于评估具有单个特征集的分类器精度。这项研究的结果显示了吱吱声和嘎嘎声音频数据集性能最好的统计特征集。本试点研究中讨论的方法将适用于车辆故障声音识别算法的开发。