Objective evaluation of interior noise booming in a passenger car based on sound metrics and artificial neural networks. 2009

Hyun-Ho Lee, and Sang-Kwon Lee
Acoustics and Noise Signal Processing Laboratory, Department of Mechanical Engineering, Inha University, 253 Yonghyun Dong, Inchon 402-751, Republic of Korea.

Booming sound is one of the important sounds in a passenger car. The aim of the paper is to develop the objective evaluation method of interior booming sound. The development method is based on the sound metrics and ANN (artificial neural network). The developed method is called the booming index. Previous work maintained that booming sound quality is related to loudness and sharpness--the sound metrics used in psychoacoustics--and that the booming index is developed by using the loudness and sharpness for a signal within whole frequency between 20 Hz and 20 kHz. In the present paper, the booming sound quality was found to be effectively related to the loudness at frequencies below 200 Hz; thus the booming index is updated by using the loudness of the signal filtered by the low pass filter at frequency under 200 Hz. The relationship between the booming index and sound metric is identified by an ANN. The updated booming index has been successfully applied to the objective evaluation of the booming sound quality of mass-produced passenger cars.

UI MeSH Term Description Entries
D008297 Male Males
D009622 Noise Any sound which is unwanted or interferes with HEARING other sounds. Noise Pollution,Noises,Pollution, Noise
D011571 Psychoacoustics The science pertaining to the interrelationship of psychologic phenomena and the individual's response to the physical properties of sound. Psychoacoustic
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000054 Acceleration An increase in the rate of speed. Accelerations
D001336 Automobiles A usually four-wheeled automotive vehicle designed for passenger transportation. Cars,Automobile,Car
D013016 Sound A type of non-ionizing radiation in which energy is transmitted through solid, liquid, or gas as compression waves. Sound (acoustic or sonic) radiation with frequencies above the audible range is classified as ultrasonic. Sound radiation below the audible range is classified as infrasonic. Acoustic Waves,Elastic Waves,Sonic Radiation,Sound Waves,Acoustic Wave,Elastic Wave,Radiation, Sonic,Radiations, Sonic,Sonic Radiations,Sound Wave,Sounds,Wave, Acoustic,Wave, Elastic,Wave, Sound,Waves, Acoustic,Waves, Elastic,Waves, Sound
D013223 Statistics as Topic Works about the science and art of collecting, summarizing, and analyzing data that are subject to random variation. Area Analysis,Estimation Technics,Estimation Techniques,Indirect Estimation Technics,Indirect Estimation Techniques,Multiple Classification Analysis,Service Statistics,Statistical Study,Statistics, Service,Tables and Charts as Topic,Analyses, Area,Analyses, Multiple Classification,Area Analyses,Classification Analyses, Multiple,Classification Analysis, Multiple,Estimation Technic, Indirect,Estimation Technics, Indirect,Estimation Technique,Estimation Technique, Indirect,Estimation Techniques, Indirect,Indirect Estimation Technic,Indirect Estimation Technique,Multiple Classification Analyses,Statistical Studies,Studies, Statistical,Study, Statistical,Technic, Indirect Estimation,Technics, Estimation,Technics, Indirect Estimation,Technique, Estimation,Technique, Indirect Estimation,Techniques, Estimation,Techniques, Indirect Estimation
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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