scholarly journals Fundamental study on sound source localization inside a structure using a deep neural network and computer-aided engineering

2021 ◽  
Vol 513 ◽  
pp. 116400
Author(s):  
Shunsuke Kita ◽  
Yoshinobu Kajikawa
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Deep Neural Network ◽  
Computer Aided Engineering ◽  
Sound Source Localization ◽  
Fundamental Study ◽  
Computer Aided

Sound Source Localization Based on von-Mises-Bernoulli Deep Neural Network

2020 ◽  
Author(s):  
Kazuhiro Nakadai ◽  
Shungo Masaki ◽  
Ryosuke Kojima ◽  
Osamu Sugiyama ◽  
Katsutoshi Itoyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Deep Neural Network ◽  
Sound Source Localization ◽  
Von Mises

scholarly journals Sound Source Localization Based on SRP-PHAT Spatial Spectrum and Deep Neural Network

2020 ◽  
Vol 64 (1) ◽  
pp. 253-271
Author(s):  
Xiaoyan Zhao ◽  
Shuwen Chen ◽  
Lin Zhou ◽  
Ying Chen
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Deep Neural Network ◽  
Spatial Spectrum ◽  
Sound Source Localization

Assessment of von Mises-Bernoulli Deep Neural Network in Sound Source Localization

2021 ◽  
Author(s):  
Katsutoshi Itoyama ◽  
Yoshiya Morimoto ◽  
Shungo Masaki ◽  
Ryosuke Kojima ◽  
Kenji Nishida ◽  
...  
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Deep Neural Network ◽  
Sound Source Localization ◽  
Von Mises

scholarly journals An Approach for Sound Source Localization by Complex-Valued Neural Network

2013 ◽  
Vol E96.D (10) ◽  
pp. 2257-2265 ◽  
Author(s):  
Hirofumi TSUZUKI ◽  
Mauricio KUGLER ◽  
Susumu KUROYANAGI ◽  
Akira IWATA
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Sound Source Localization ◽  
Complex Valued

Indoor Sound Source Localization Algorithm Based on BP Neural Network

2021 ◽  
Author(s):  
Lan Wang ◽  
Kun Zhang ◽  
Chong Shen ◽  
Chai Wang ◽  
Xixi Fu
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Bp Neural Network ◽  
Sound Source Localization ◽  
Localization Algorithm

scholarly journals Indoor Sound Source Localization With Probabilistic Neural Network

2018 ◽  
Vol 65 (8) ◽  
pp. 6403-6413 ◽  
Author(s):  
Yingxiang Sun ◽  
Jiajia Chen ◽  
Chau Yuen ◽  
Susanto Rahardja
Keyword(s):  
Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Probabilistic Neural Network ◽  
Sound Source Localization

scholarly journals DOANet: a deep dilated convolutional neural network approach for search and rescue with drone-embedded sound source localization

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Alif Bin Abdul Qayyum ◽  
K. M. Naimul Hassan ◽  
Adrita Anika ◽  
Md. Farhan Shadiq ◽  
Md Mushfiqur Rahman ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Performance Indicator ◽  
Angular Spectrum ◽  
Search And Rescue ◽  
Sound Source Localization ◽  
A Performance

Abstract Drone-embedded sound source localization (SSL) has interesting application perspective in challenging search and rescue scenarios due to bad lighting conditions or occlusions. However, the problem gets complicated by severe drone ego-noise that may result in negative signal-to-noise ratios in the recorded microphone signals. In this paper, we present our work on drone-embedded SSL using recordings from an 8-channel cube-shaped microphone array embedded in an unmanned aerial vehicle (UAV). We use angular spectrum-based TDOA (time difference of arrival) estimation methods such as generalized cross-correlation phase-transform (GCC-PHAT), minimum-variance-distortion-less-response (MVDR) as baseline, which are state-of-the-art techniques for SSL. Though we improve the baseline method by reducing ego-noise using speed correlated harmonics cancellation (SCHC) technique, our main focus is to utilize deep learning techniques to solve this challenging problem. Here, we propose an end-to-end deep learning model, called DOANet, for SSL. DOANet is based on a one-dimensional dilated convolutional neural network that computes the azimuth and elevation angles of the target sound source from the raw audio signal. The advantage of using DOANet is that it does not require any hand-crafted audio features or ego-noise reduction for DOA estimation. We then evaluate the SSL performance using the proposed and baseline methods and find that the DOANet shows promising results compared to both the angular spectrum methods with and without SCHC. To evaluate the different methods, we also introduce a well-known parameter—area under the curve (AUC) of cumulative histogram plots of angular deviations—as a performance indicator which, to our knowledge, has not been used as a performance indicator for this sort of problem before.

scholarly journals Robust Sound Source Localization Using Convolutional Neural Network Based on Microphone Array

2021 ◽  
Vol 29 (3) ◽  
pp. 361-371
Author(s):  
Xiaoyan Zhao ◽  
Lin Zhou ◽  
Ying Tong ◽  
Yuxiao Qi ◽  
Jingang Shi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Sound Source Localization
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