Composite Model Particle Filter for Indoor Sound Source Location Based on Multi-feature

doi:10.12382/bgxb.2022.0849

Abstract

Abstract:

A multi-feature-based composite model particle filter algorithm is proposed to improve the accuracy and robustness of sound source location in reverberation and noise environment. In this algorithm, the likelihood function of the particle filter is constructed based on the multiple features of signal received by a microphone, where the depth features of multiple hypothesis time-delay estimated image are extracted by convolutional neural network (CNN), and a time-delay estimation model based on support vector regression (SVR) is established. Furthermore, the deficiency that single feature can’t suppress noise and reverberation simultaneously is remedied by introducing the beam output energy fusion mechanism. For the randomness of speaker motion, a composite model for sound source tracking is established to improve the robustness of speaker tracking system. The simulated and experimental results show that, based on the composite model, the position average root mean square error (RMSE) of multi-feature algorithm is reduced by more than 83% compared with that of steered response power and time delay estimation (SRPTDE) algorithm, and under multi-feature observation, the position average RMSE of composite model is reduced by more than 46% compared with that of Langevin model and the random walking model. The proposed algorithm realizes the effective tracking of random moving sound sources in complex environment.

Key words: indoor sound source localization, multi-feature, time delay estimation, composite model, particle filter

CLC Number:

TN272

LIU Wangsheng, PAN Haipeng, WANG Minghuan. Composite Model Particle Filter for Indoor Sound Source Location Based on Multi-feature[J]. Acta Armamentarii, 2024, 45(3): 975-985.

Figures/Tables 13

References 23

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网络层	网络参数
输入层	输入维数:28×28
卷积层H₁	卷积面个数:6;卷积核大小:5×5
池化层H₂	过滤器尺寸:2×2;滑动步长:2
卷积层H₃	卷积面个数:12;卷积核大小:5×5
池化层H₄	过滤器尺寸:2×2;滑动步长:2
全连接层H₅	输出维数:192×1

网络层	网络参数
输入层	输入维数:28×28
卷积层H₁	卷积面个数:6;卷积核大小:5×5
池化层H₂	过滤器尺寸:2×2;滑动步长:2
卷积层H₃	卷积面个数:12;卷积核大小:5×5
池化层H₄	过滤器尺寸:2×2;滑动步长:2
全连接层H₅	输出维数:192×1

算法	转向机动位置平均RMSE/m	圆弧机动位置平均RMSE/m
MFL	0.1976	0.1538
MFRW	0.2065	0.1543
MFCV	0.4073	0.5511
MFCM	0.0841	0.0819

算法	转向机动位置平均RMSE/m	圆弧机动位置平均RMSE/m
MFL	0.1976	0.1538
MFRW	0.2065	0.1543
MFCV	0.4073	0.5511
MFCM	0.0841	0.0819

算法	空间复杂度	时间复杂度	运行时间/s
MFL	O(n⁴n_a+p)	O(n⁴n_a+p)	2.4757
MFRW	O(n⁴n_a+p)	O(n⁴n_a+p)	2.1241
MFCV	O(n⁴n_a+p)	O(n⁴n_a+p)	2.2353
MFCM	O(n⁴n_a+p)	O(n⁴n_a+p)	12.141