Adaptive Short Time Fourier Transform (STFT) Analysis of seismic electric signal (SES): A comparison of Hamming and rectangular window

Astuti, Winda; Sediono, Wahju; Aibinu, Abiodun Musa; Akmeliawati, Rini; Salami, Momoh Jimoh Eyiomika

doi:10.1109/isiea.2012.6496663

Cited by 16 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The spectrum corresponding to the original signal and the signal after framing are very different. The Hamming window can effectively overcome the leakage phenomenon Astuti et al (2012) . The Hamming window function W(n) is shown as follows

01

where the α value is 0.46 by suggested in Trang et al (2014) .…”

Section: Proposed Methodologymentioning

confidence: 99%

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Zhang

Wang

et al. 2022

Front. Physiol.

View full text Add to dashboard Cite

Heart sound classification plays a critical role in the early diagnosis of cardiovascular diseases. Although there have been many advances in heart sound classification in the last few years, most of them are still based on conventional segmented features and shallow structure-based classifiers. Therefore, we propose a new heart sound classification method based on improved mel-frequency cepstrum coefficient features and deep residual learning. Firstly, the heart sound signal is preprocessed, and its improved features are computed. Then, these features are used as input features of the neural network. The pathological information in the heart sound signal is further extracted by the deep residual network. Finally, the heart sound signal is classified into different categories according to the features learned by the neural network. This paper presents comprehensive analyses of different network parameters and network connection strategies. The proposed method achieves an accuracy of 94.43% on the dataset in this paper.

show abstract

01

where the α value is 0.46 by suggested in Trang et al (2014) .…”

Section: Proposed Methodologymentioning

confidence: 99%

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Zhang

Wang

et al. 2022

Front. Physiol.

View full text Add to dashboard Cite

show abstract

“…The window type, window size, overlap, and FFT size parameters are set to Hamming, 480, 240, and 480, respectively. The Hamming window is adopted for signal framing as it can effectively overcome the leakage phenomenon [34]. The dimension of the As can be seen from the confusion matrix in Figure 5, the abscissa is the true class, and the ordinate is the predicted class.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Neural Network Model Based on the Tensor Network for Audio Tagging of Domestic Activities

et al. 2022

View full text Add to dashboard Cite

Due to the serious problem of population aging, monitoring of domestic activities is increasingly important. Audio tagging of domestic activities is very suitable when the visual data are unavailable due to the interference from light and the environment. Aiming at solving this problem, a neural network model based on the tensor network is proposed for audio tagging of domestic activities that is more interpretable than traditional neural networks. The introduction of the tensor network can compress the network parameters and reduce the redundancy of the training model while maintaining a good performance. First, the important features of a Mel spectrogram of the input audio are extracted through the convolutional neural networks (CNNs). Then, they are converted into the high-order space corresponding with the tensor network. The spatial structure information and important features can be further extracted and retained through the matrix product state (MPS). Large patches of the featured data are divided into small local orderless patches when using the tensor network. The final tagging results are obtained through the MPS layers which is just a tensor network structure based on the tensor train decomposition. In order to evaluate the proposed method, the DCASE 2018 challenge task 5 dataset for monitoring domestic activities is selected. The results showed that the average F1-score of the proposed model in the test set of the development dataset and validation dataset reached 87.7 and 85.9%, which are 3.2 and 2.8% higher than the baseline system, respectively. It is verified that the proposed model can perform better and more efficiently for audio tagging of domestic activities.

show abstract

“…Figure 2 shows the relationship between frame shift and frame length. A window function [ 48 ] is a function used to reduce signal interruption at the beginning and end of each frame. This is done by considering the next frame and integrating the frequency lines, thus making each frame smoothly interconnected.…”

Section: Proposed Fault Diagnosis Methodsmentioning

confidence: 99%

LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers

Yang

Liu³

et al. 2022

Sensors

View full text Add to dashboard Cite

Power fault monitoring based on acoustic waves has gained a great deal of attention in industry. Existing methods for fault diagnosis typically collect sound signals on site and transmit them to a back-end server for analysis, which may fail to provide a real-time response due to transmission packet loss and latency. However, the limited computing power of edge devices and the existing methods for feature extraction pose a significant challenge to performing diagnosis on the edge. In this paper, we propose a fast Lightweight Fault Diagnosis method for power transformers, referred to as LightFD, which integrates several technical components. Firstly, before feature extraction, we design an asymmetric Hamming-cosine window function to reduce signal spectrum leakage and ensure data integrity. Secondly, we design a multidimensional spatio-temporal feature extraction method to extract acoustic features. Finally, we design a parallel dual-layer, dual-channel lightweight neural network to realize the classification of different fault types on edge devices with limited computing power. Extensive simulation and experimental results show that the diagnostic precision and recall of LightFD reach 94.64% and 95.33%, which represent an improvement of 4% and 1.6% over the traditional SVM method, respectively.

show abstract

Adaptive Short Time Fourier Transform (STFT) Analysis of seismic electric signal (SES): A comparison of Hamming and rectangular window

Cited by 16 publications

References 3 publications

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Neural Network Model Based on the Tensor Network for Audio Tagging of Domestic Activities

LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers

Contact Info

Product

Resources

About