Detection Sound Source Direction in 3D Space Using Convolutional Neural Networks

被引：0
|
作者：
Yue, Xiao [1 ]
Qu, Guangzhi [1 ]
Liu, Bo [2 ]
Liu, Anyi [1 ]
机构：
[1] Oakland Univ, Comp Sci & Engn Dept, Rochester, MI 48063 USA
[2] Beijing Univ Technol, Sch Software Engn, Fac Informat Technol, Beijing, Peoples R China
来源：
2018 FIRST IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR INDUSTRIES (AI4I 2018) | 2018年
关键词：
Sound Source Direction Detection; GCC-PHAT; Convolutional Neural Network; Room impulse simulation;
D O I：
10.1109/ai4i.2018.00027
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Sound source detection and localization have a lot of practical uses in many industrial settings. Most of sound source direction detection algorithms in literature are designed to identify the angle of sound source in a 2D space. In this work, we propose to use convolutional neural networks to detect the sound source direction in a 3D space. This algorithm is based on the generalized cross correlation method with phase transform (GCC-PHAT) [1] to derive time delay of arrival (TDOA). By using a convolutional neural network model, this algorithm can be applied and deployed. In addition, by modifying GCC-PHAT formula, this approach also works of multiple sound sources detection. Simulation experimental results on single sound source and multiple sound sources detection show the proposed system could work in most situations.
引用
收藏
页码：81 / 84
页数：4
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