Adapted waveform analysis as a tool for modeling, feature extraction, and denoising

1994 ◽  
Vol 33 (7) ◽  
pp. 2170 ◽  
Author(s):  
Mladen V. Wickerhauser
Keyword(s):  
Feature Extraction ◽  
Waveform Analysis ◽  
Modeling Feature

Adaptive Gaussian Mixture Model and Convolution Autoencoder Clustering for Unsupervised Seismic Waveform Analysis

2021 ◽  
pp. 1-46
Author(s):  
Donglin Zhu ◽  
Jingbin Cui ◽  
Yan Li ◽  
Zhonghong Wan ◽  
Lei Li
Keyword(s):  
Feature Extraction ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Facies Analysis ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Waveform Analysis ◽  
Western China ◽  
Reservoir Properties ◽  
Seismic Waveform

Seismic facies analysis can effectively estimate reservoir properties and seismic waveform clustering is a useful tool for facies analysis. We developed a deep learning-based clustering approach called the modified deep convolutional embedded clustering with adaptive Gaussian mixture model (AGMM-MDCEC) for seismic waveform clustering. Trainable feature extraction and clustering layers in AGMM-MDCEC are implemented using neural networks. The two independent processes of feature extraction and clustering are fused, such that extracted features are modified simultaneously with the results of clustering. A convolutional autoencoder is used in the algorithm for extracting features from seismic data and reduce data redundancy. At the same time, weights of clustering network are fined-tuned through iteration to obtain state-of-the-art clustering results. We apply our new classification algorithm to a data volume acquired in western China to map architectural elements of a complex fluvial depositional system. Our proposed method obtains superior results over those provided by traditional K-means, Gaussian mixture model, and some machine learning methods, and improves the mapping of the extent of the distributary system.

scholarly journals Digital Music Waveform Analysis and Retrieval Based on Feature Extraction Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Jiao
Keyword(s):  
Feature Extraction ◽  
Leading Edge ◽  
Extraction Process ◽  
Waveform Analysis ◽  
Digital Music ◽  
Feature Extraction Algorithm ◽  
Starting Point ◽  
Depth Analysis ◽  
Extraction Algorithm ◽  
Extraction Model

In order to improve the feature extraction effect of digital music and improve the efficiency of music retrieval, this paper combines digital technology to analyze music waveforms, extract music features, and realize digital processing of music features. Taking the extraction of waveform music file features as the starting point, this paper combines the digital music feature extraction algorithm to build a music feature extraction model and conducts an in-depth analysis of the digital music waveform extraction process. In addition, by setting the threshold, the linear difference between the sampling points on both sides of the threshold on the leading edge of the waveform is used to obtain the overthreshold time. From the experimental research results, it can be seen that the music feature extraction model based on digital music waveform analysis proposed in this paper has good results.

scholarly journals Software-hardware analysis of signal feature classification algorithms

2021 ◽  
Author(s):  
Hamidreza Asefi-Ghamari
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Extraction Methods ◽  
Suitable Method ◽  
Scene Analysis ◽  
Feature Analysis ◽  
Analysis Tool ◽  
Real Time System ◽  
Analysis Application ◽  
Modeling Feature

Over the last few decades, signal feature analysis has been significantly used in a wide variety of fields. While several techniques have been proposed in the area of signal feature extraction and classification, all of these techniques are achieved by using modern computers, which rely on softwares, such as MATLAB. However, in real-time applications or portable devices, software implementation is not enough by itself, and a hardware-software co-design or fully hardware implementation needs to be considered. The selection of the right signal feature analysis tool for an application depends not only on the software performance, but also on the hardware efficiency of a method. However, there is not enough studies in existence to provide comparison of these signal feature extraction methods from the hardware implentation aspect. Therefore, the objective of this thesis is to investigate both the hardware and algorithmic perspectives of three commonly used signal feature extraction techniques: Autoregressive (AR), pole modeling, and Mel-frequency Cepstral coefficients (MFCCs). To fulfill this objective, first, the hardware analysis of AR, pole modeling, and MFCC feature extraction methods is performed by calculating the computational complexity of the mathematical equations of each method. Second the FPGA area usage of each feature extraction methods is estimated. Third, algorithmic evaluation of these three methods is performed for audio scene analysis. Once the results are obtained from the above stages, the overall performance of each feature extraction method is compared in terms of both the hardware analysis and algorithmic performances. Finally, based on the performed comparison, pole modeling feature extraction approach is proposed as the suitable method for the audio scene analysis application. The suitable method (pole modeling feature extraction) + linear discriminant analysis (LDA) classifier are implemented in Altera DE2 Board using Altera Nios II soft-core processor. The audio classification accuracy obtained using this implementation is achieved to be equal to the MATLAB implementation. The classification time for one audio sample is determined to be 0.1s, which is fast enough to be considered as a real-time system for audio scene analysis application.

Feature extraction and waveform analysis of the optoelectronic liquid drop fingerprint

2009 ◽  
Author(s):  
Qing Song ◽  
Jing Liu ◽  
Di Wu ◽  
Jiayong Huang ◽  
Chunsong Zhang
Keyword(s):  
Feature Extraction ◽  
Liquid Drop ◽  
Waveform Analysis

scholarly journals Feature Extraction and Classification Methods for Lung Sounds

2020 ◽  
Vol 10 (1) ◽  
pp. 128-137
Keyword(s):  
Feature Extraction ◽  
Lung Diseases ◽  
Waveform Analysis ◽  
Lung Sounds ◽  
Correct Treatment ◽  
Hilbert Huang Transform ◽  
Chronic Lung Diseases ◽  
Pulmonary Disorder ◽  
Non Stationary Signal

The lung sounds is a non-stationary signal. It is a major challenge to analyze and differentiate the type of pulmonary disorder based on lung sounds. This paper presents a detailed review of existing methods of feature extraction and classification of Lung sounds for diagnosing the various types of pulmonary disorder. The different methods like spectral analysis, Cepstrum and Mel- Cepstrum, Hilbert Huang Transform, Spectrogram and 2D representation, Wavelet method, time expanded waveform analysis, Hidden Markov model, Auto Regressive model, and Neural Network are being discussed here. All the discussed methods automatically recognise the different types of lung sounds and pulmonary disorder based on features extracted from recorded lung sounds. The paper covered all the suited existing methods which can effectively detect the lung diseases. As per the result of this analysis, it has been found that still more work is required to be done in the screening and classification of chronic Lung diseases. Chronic lung diseases, having similar symptoms and which are very hard to be distinguished and classified. So, therefore, some suitable work needed to be done so that it could effectively support the physicians for taking diagnosis decisions and for giving the correct treatment without any delay in such chronic diseases also

scholarly journals Software-hardware analysis of signal feature classification algorithms

2021 ◽  
Author(s):  
Hamidreza Asefi-Ghamari
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Extraction Methods ◽  
Suitable Method ◽  
Scene Analysis ◽  
Feature Analysis ◽  
Analysis Tool ◽  
Real Time System ◽  
Analysis Application ◽  
Modeling Feature

Over the last few decades, signal feature analysis has been significantly used in a wide variety of fields. While several techniques have been proposed in the area of signal feature extraction and classification, all of these techniques are achieved by using modern computers, which rely on softwares, such as MATLAB. However, in real-time applications or portable devices, software implementation is not enough by itself, and a hardware-software co-design or fully hardware implementation needs to be considered. The selection of the right signal feature analysis tool for an application depends not only on the software performance, but also on the hardware efficiency of a method. However, there is not enough studies in existence to provide comparison of these signal feature extraction methods from the hardware implentation aspect. Therefore, the objective of this thesis is to investigate both the hardware and algorithmic perspectives of three commonly used signal feature extraction techniques: Autoregressive (AR), pole modeling, and Mel-frequency Cepstral coefficients (MFCCs). To fulfill this objective, first, the hardware analysis of AR, pole modeling, and MFCC feature extraction methods is performed by calculating the computational complexity of the mathematical equations of each method. Second the FPGA area usage of each feature extraction methods is estimated. Third, algorithmic evaluation of these three methods is performed for audio scene analysis. Once the results are obtained from the above stages, the overall performance of each feature extraction method is compared in terms of both the hardware analysis and algorithmic performances. Finally, based on the performed comparison, pole modeling feature extraction approach is proposed as the suitable method for the audio scene analysis application. The suitable method (pole modeling feature extraction) + linear discriminant analysis (LDA) classifier are implemented in Altera DE2 Board using Altera Nios II soft-core processor. The audio classification accuracy obtained using this implementation is achieved to be equal to the MATLAB implementation. The classification time for one audio sample is determined to be 0.1s, which is fast enough to be considered as a real-time system for audio scene analysis application.

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