Results 251 to 260 of about 735,517 (309)

Features extraction for speech emotion

Journal of Computational Methods in Sciences and Engineering, 2009
In this paper the speech emotion verification using two most popular methods in speech processing and analysis based on the Mel-Frequency Cepstral Coefficient (MFCC) and the Gaussian Mixture Model (GMM) were proposed and analyzed. In both cases, features for the speech emotion were extracted using the Short Time Fourier Transform (STFT) and Short Time ...
Norhaslinda Kamaruddin, Abdul Wahab 0001
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A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

Applied Sciences (Switzerland), 2019
Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are “hand-crafted”, which are difficult in features selection and poor in stability. The lack of automatic, efficient and
Yuanhong Zhong, Xinyu Cheng
exaly   +2 more sources

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Feature Selection and Feature Extraction: Highlights

Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 2021
In recent years, big data deluges have resulted in exciting data science opportunities. In particular, there is always a desire to extract the most from different data sources. To address it, a promising and recurring task is to perform feature selection and feature extraction.
Hiu-Man Wong, Xingjian Chen, Hiu-Hin Tam, Jiecong Lin, Shixiong Zhang 0002, Shankai Yan, Xiangtao Li, Ka-Chun Wong   +7 more
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Systematic Feature Extraction

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982
A systematic feature extraction procedure is proposed. It is based on successive extractions of features. At each stage a dimensionality reduction is made and a new feature is extracted. A specific example is given using the Gaussian minus-log-likelihood ratio as a basis for the extracted features.
Kenneth A. Brakke, James M. Mantock, Keinosuke Fukunaga   +2 more
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Feature extraction in the Neocognitron

IEEE International Conference on Neural Networks, 1988
The authors present theoretical and numerical developments in the understanding of feature extraction in the Neocognitron. First, they show that the feature extraction process is equivalent to a generalized nonlinear discriminant. Second, they show that the operation of the feature-extraction process can be linked to the eigenvectors and eigenvalues of
Ken Johnson, Cindy Daniell, Jerry Burman
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Feature Extraction

2016
Most original work on feature extraction has its root in classical 2D image processing (Sec.1) and mainly focuses on edge detection and the localization of interest points and regions. In practice, extracting these features corresponds to segment the image and to analyze its content.
S Biasotti, A Cerri, G Patanè, M Spagnuolo   +3 more
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Texture feature extraction

Pattern Recognition Letters, 1987
Abstract We present a new approach to texture feature extraction from a cooccurrence matrix. Computationally, the method is much faster than traditional uses of cooccurrence matrices. Using Brodatz's textures, the proposed features are evaluated and compared with those suggested by Conners et al. (1984).
Dong-Chen He, Li Wang 0002, Jean Guibert
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