Comparison between fabric frictional sound parameters and wearers’ subjective auditory sensibility of PCM-treated combat uniforms

An innovative Frictional Sound Automatic Measuring System (FSAMS) was designed to collect and enable analysis of the frictional sound spectra of four natural fibre woven fabrics which included cotton, linen, silk, and wool. The Fast Fourier Transform (FFT) method was used to convert time-domain signals into frequency-domain signals to enable the maximum sound amplitude (MSA) and the level pressure of the total sound (LPTS) of the cotton, linen, silk, and wool fabrics to be calculated and analysed. Subsequently auto-regression formulae were used to calculate the fabric auto-regressive coefficients (ARC, ARF, and ARE); the correlations between fabric frictional sound in terms of LPTS and AR coefficients, and mechanical properties as measured by KES-FB were also evaluated. Stepwise regression was then used to identify the key frictional sound parameters for the four types of fabric. The results show that LPTS values for cotton, linen, silk, and wool fabrics increase with their ARC values. It was revealed that the key mechanical parameters affecting fabric frictional sound for the four natural fibre woven fabrics were not the same for each fabric type: the parameters that influenced LPTS values were the fabric weight and bending hysteresis for the cotton fabric, tensile energy for the linen, tensile resilience for the silk and shear hysteresis at a 5° shear angle for the wool fabric.

Download Full-text

Discrimination of fabric frictional sounds based on Haar features

Textile Research Journal ◽

10.1177/0040517518786276 ◽

2018 ◽

Vol 89 (11) ◽

pp. 2067-2074 ◽

Cited By ~ 1

Author(s):

Chen Tao ◽

Yafeng Duan ◽

Xinghua Hong

Keyword(s):

Feature Space ◽

Cotton Wool ◽

Huge Number ◽

Challenging Issue ◽

Haar Features ◽

Haar Feature ◽

Heavy Burden ◽

Frictional Sound ◽

Fabric Materials

In allusion to the challenging issue of identifying fabric materials by frictional sounds, this study endeavors to prove the possibility of classifying fabric friction sounds into their material categories using discriminators built upon the Haar features. A total of 32 pieces of fabric falling into four material categories including cotton, wool, silk, and flax are put through a specialized apparatus to collect frictional sound signals. The Haar features on every scale and position of the acquired signal are extracted to establish a feature space. For each point in the feature space, a discriminator is built to approve all positive samples of a certain category and deny as many negative samples as possible. To relieve the heavy burden produced by the huge number of discriminators, progressive selection is performed on the discriminators to form a queue in which a discriminator is liable to fix some errors of the former. The outcome is a much-reduced version of the unordered discriminators with the same discriminability. The improved Haar feature is also investigated and is found to be capable of reducing the size of the discrimination queue, thus further improving the efficiency of the mechanism. It is also revealed that additional samples involved can help achieve a perfect accuracy. The discrimination mechanism advanced by this effort can provide a basis for identifying fabric materials by frictional sounds.

Download Full-text