Seismic Event and Phase Detection Using Time–Frequency Representation and Convolutional Neural Networks

2019 ◽  
Vol 90 (2A) ◽  
pp. 481-490 ◽  
Author(s):  
Ramin M. H. Dokht ◽  
Honn Kao ◽  
Ryan Visser ◽  
Brindley Smith
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Seismic Event ◽  
Phase Detection ◽  
Time Frequency ◽  
Frequency Representation

scholarly journals Configuration-Invariant Sound Localization Technique Using Azimuth-Frequency Representation and Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3768
Author(s):  
Chanjun Chun ◽  
Kwang Myung Jeon ◽  
Wooyeol Choi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Sound Localization ◽  
Speech Processing ◽  
Image Method ◽  
Frame Size ◽  
Localization Technique ◽  
Time Frequency ◽  
Input Size ◽  
Frequency Representation

Deep neural networks (DNNs) have achieved significant advancements in speech processing, and numerous types of DNN architectures have been proposed in the field of sound localization. When a DNN model is deployed for sound localization, a fixed input size is required. This is generally determined by the number of microphones, the fast Fourier transform size, and the frame size. if the numbers or configurations of the microphones change, the DNN model should be retrained because the size of the input features changes. in this paper, we propose a configuration-invariant sound localization technique using the azimuth-frequency representation and convolutional neural networks (CNNs). the proposed CNN model receives the azimuth-frequency representation instead of time-frequency features as the input features. the proposed model was evaluated in different environments from the microphone configuration in which it was originally trained. for evaluation, single sound source is simulated using the image method. Through the evaluations, it was confirmed that the localization performance was superior to the conventional steered response power phase transform (SRP-PHAT) and multiple signal classification (MUSIC) methods.

scholarly journals Getting the gist faster: Blurry images enhance the early temporal similarity between neural signals and convolutional neural networks

2021 ◽  
Author(s):  
David A. Tovar ◽  
Tijl Grootswagers ◽  
James Jun ◽  
Oakyoon Cha ◽  
Randolph Blake ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Brain Activity ◽  
Visual Image ◽  
High Temporal Resolution ◽  
Neural Signals ◽  
Time Frequency ◽  
Brain Signals ◽  
Spatial Frequencies ◽  
Full Picture

Humans are able to recognize objects under a variety of noisy conditions, so models of the human visual system must account for how this feat is accomplished. In this study, we investigated how image perturbations, specifically reducing images to their low spatial frequency (LSF) components, affected correspondence between convolutional neural networks (CNNs) and brain signals recorded using magnetoencephalography (MEG). Using the high temporal resolution of MEG, we found that CNN-Brain correspondence for deeper and more complex layers across CNN architectures emerged earlier for LSF images than for their unfiltered broadband counterparts. The early emergence of LSF components is consistent with the coarse-to-fine theoretical framework for visual image processing, but surprisingly shows that LSF signals from images are more prominent when high spatial frequencies are removed. In addition, we decomposed MEG signals into oscillatory components and found correspondence varied based on frequency bands, painting a full picture of how CNN-Brain correspondence varies with time, frequency, and MEG sensor locations. Finally, we varied image properties of CNN training sets, and found marked changes in CNN processing dynamics and correspondence to brain activity. In sum, we show that image perturbations affect CNN-Brain correspondence in unexpected ways, as well as provide a rich methodological framework for assessing CNN-Brain correspondence across space, time, and frequency.

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