.png){kind=icon}
Return
Sound source localization method based time-domain signal feature using deep learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apacoust.2023.109626.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Deep learning, as the most commonly used machine learning algorithm, is widely used in various fields. In the field of acoustics, deep learning methods are combined with frequency-domain features of signals to locate sound sources. The commonly frequency domain features include microphones array Cross-spectral-Matrix(CSM) and Short Time Fourier Transform(STFT). However, the use of frequency-domain features often leads to the loss of partial signal information and increases the computational complexity. This paper proposed a novel sound source localization algorithm based on time-domain features, which uses convolutional neural network(CNN) as a medium to achieve mapping from time-domain features to sound source locations. This method does not rely on any basic signal processing algorithm, and directly uses time-domain sampling points as network inputs for sound source localization. The application simulation shows that the proposed method can achieve precise localization and low side-lobe effect under different testing conditions. Once the network training is completed, the testing accuracy under different conditions is above 95%, with a maximum of 100%.
Keywords:
Sound source localization
Microphone array
Time-domain features
Convolutional nerual network
Journal
IF:
3.6
Papers:
7.4K
Citations:
1.7W
Organization
Cited Papers
ShareSaveA double-step grid-free method for sound source identification using deep learning.png)
APPLIED ACOUSTICS
APPLIED ACOUSTICSIF3.6
PREAI
ShareSave
ShareSaveA neural network based microphone array approach to grid-less noise source localizationAPPLIED ACOUSTICS
APPLIED ACOUSTICSIF3.6
PREAI
ShareSave
ShareSaveCompression computational grid based on functional beamforming for acoustic source localizationAPPLIED ACOUSTICS
APPLIED ACOUSTICSIF3.6
PREAI
ShareSave
View more