.png){kind=icon}
返回
Deep Learning Aided Sound Source Localization: A Nonsynchronous Measurement Approach
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2023.3273657.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nonsynchronous measurement (NSM) is able to improve the spatial resolution of beamforming and localization accuracy at the cost of longer recording time. This trade-off can be tolerated in various applications of sound source localization (SSL), such as machine fault diagnosis. Therefore, the NSM is a promising localization technique. For stationary acoustic field, the state-of-the-art NSM methods can well localize the stationary sources. However, when the sound sources are nonstationary, these conventional NSM algorithms fail to work effectively. In this work, we propose deep-learning-aided NSM (DL-NSM) to address the nonstationary source localization. Similar to the conventional NSM methods, the DL-NSM utilizes the diagonal blocks of an incomplete covariance matrix of the NSM to determine the nondiagonal elements of this matrix but differs in the fact that the DL-NSM can achieve the approximation to the covariance matrix of the corresponding synchronous measurement (SM). In the corresponding SM, a prototype array with a larger size and denser array elements is utilized to localize the sound sources, i.e., the covariance matrix of the corresponding SM can give the correlations of all array elements. This nonlinear approximation of the covariance matrix in the proposed DL-NSM can be learned by the proper training procedure. Therefore, the correlations of the NSM could be recovered, and the array aperture might be expanded, providing high-resolution localization. The simulation and experiment validate the superiority of the proposed DL-NSM.
Keyword:
Covariance matrices
Location awareness
Optimization
Microphone arrays
Time measurement
Prototypes
Eigenvalues and eigenfunctions
Covariance matrix completion
deep learning (DL)
Index Terms
nonstationary sound source localization (SLL)
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
Nyquist-Shannon sampling theorem applied to refinements of the atomic pair distribution function.png)
PHYSICAL REVIEW B
OAAI
PHYSICAL REVIEW BIF3.7
OAAI分享收藏A Fast and Robust Localization Method for Low-Frequency Acoustic Source: Variational Bayesian Inference Based on Nonsynchronous Array Measurements一种快速稳健的低频声源定位方法: 基于非同步阵列测量的变分贝叶斯推理
PREAI
分享收藏N-Doped Graphene-Supported PtRu Direct Methanol Fuel Cell Electrocatalysts氮掺杂石墨烯负载PtRu直接甲醇燃料电池电催化剂
OAAI
OAAI分享收藏
查看更多内容