Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion

Li, Yanli; Mei, Lala; Li, Ran; Wu, Chang-an

doi:10.3390/fi10090084

Cited by 2 publications

(1 citation statement)

References 16 publications

(24 reference statements)

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“…The process is similar to deconvolution, which can reverse the convolution process and extract the real earthquake pulse signals from the mixed seismic records and artificial pulse signals [33][34][35][36]. To understand the randomness in the signal, we use the median absolute deviation (MAD) value to determine the minimum threshold of the wavelet coefficients in the time series [37,38]. Then, we filter out the low coefficients from the wavelet coefficients and reconstruct the real earthquake signals from the remaining coefficients.…”

Section: Removing Noisementioning

confidence: 99%

Predicting the Remaining Time before Earthquake Occurrence Based on Mel Spectrogram Features Extraction and Ensemble Learning

Zhang,

Xu,

Chen

et al. 2023

Applied Sciences

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Predicting the remaining time before the next earthquake based on seismic signals generated in a laboratory setting is a challenging research task that is of significant importance for earthquake hazard assessment. In this study, we employed a mel spectrogram and the mel frequency cepstral coefficient (MFCC) to extract relevant features from seismic signals. Furthermore, we proposed a deep learning model with a hierarchical structure. This model combines the characteristics of long short-term memory (LSTM), one-dimensional convolutional neural networks (1D-CNN), and two-dimensional convolutional neural networks (2D-CNN). Additionally, we applied a stacking model fusion strategy, combining gradient boosting trees with deep learning models to achieve optimal performance. We compared the performance of the aforementioned feature extraction methods and related models for earthquake prediction. The results revealed a significant improvement in predictive performance when the mel spectrogram and stacking were introduced. Additionally, we found that the combination of 1D-CNN and 2D-CNN has unique advantages in handling time-series problems.

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