Geometric model of human factor parameters for channel identification for active noise control

被引：0

作者：

Wang S. ^{[1
]}

Xu J. ^{[1
]}

Gao Y. ^{[2
]}

Li C. ^{[1
]}

Lin Q. ^{[1
]}

Gao D. ^{[1
]}

Zhang J. ^{[1
]}

Jin B. ^{[1
]}

机构：

[1] Research Center for Humanoid Sensing, Zhejiang Laboratory, Hangzhou

[2] Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing

来源：

Shengxue Xuebao/Acta Acustica | 2024年 / 49卷 / 02期

关键词：

Active noise control; Anthropology; Transfer function matrix; Transfer path identification;

D O I：

10.12395/0371-0025.2023179

中图分类号：

学科分类号：

摘要：

This paper introduces an approach to acoustic channel identification rooted in a geometric model that accounts for human factors. This method holds promise for applications in active noise control technology, specifically addressing the challenge of maintaining optimal noise reduction performance despite the changes in human posture. The approach involves extracting human factor parameters from the point cloud data acquired through a depth camera, which are then used to construct a precise geometric model. This model is utilized to predict the transfer function matrix of the acoustic channel through simulation and artificial neural networks. The results demonstrate that the geometric model incorporating human factor parameters yields noise reduction performance comparable to that of the traditional point cloud models, making it an attractive option for parametric scanning simulations. Furthermore, experimental verification also validates the accuracy of the simulated results and the feasibility of the proposed approach. The findings show the significant impact of changing human posture on the complexity and variability of the acoustic channel, emphasizing the importance of the posture effects in real-world applications. © 2024 Science Press. All rights reserved.

引用

页码：226 / 237

页数：11

共 29 条

[21] Chang C Y, Chuang C T, Kuo S M, Et al., Multi-functional active noise control system on headrest of airplane seat, Mech. Syst. Signal Process, 167, pp. 1-16, (2022)
[22] Zhang J, Elliott S J, Cheer J., Robust performance of virtual sensing methods for active noise control, Mech. Syst. Signal Process, 152, pp. 1-16, (2021)
[23] Feuer A, Weinstein E., Convergence analysis of LMS filters with uncorrelated gaussian data, IEEE Trans. Acoust. Speech Signal Process, 33, 1, (1985)
[24] Becker S, Berger J, Ludwig O, Et al., Heading in soccer: does kinematics of the head-neck-torso alignment influence head acceleration, J. Hum. Kinet, 77, pp. 71-80, (2021)
[25] Burkhard M D, Sachs R M., Anthropometric manikin for acoustic research, J. Acoust. Soc. Am, 58, 1, pp. 214-222, (1975)
[26] Head and torso HRTF computation, (2023)
[27] Cai T L, Rakerd B, Hartmann W M., Computing interaural differences through finite element modeling of idealized human heads, J. Acoust. Soc. Am, 138, 3, pp. 1549-1560, (2015)
[28] Elliott S J, Cheer J., Modeling local active sound control with remote sensors in spatially random pressure fields, J. Acoust. Soc. Am, 137, 4, pp. 1936-1946, (2015)
[29] Cook R K, Waterhouse R V, Berendt R D, Et al., Measurement of correlation coefficients in reverberant sound fields, J. Acoust. Soc. Am, 27, 6, pp. 1072-1077, (1955)

← 1 2 3 →