A novel time-frequency feature extraction for movie audio signals classification

2014 International Conference on Audio, Language and Image Processing ◽

10.1109/icalip.2014.7009745 ◽

2014 ◽

Author(s):

Jichen Yang ◽

Qianhua He ◽

Min Cai ◽

Yanxiong Li

Keyword(s):

Feature Extraction ◽

Audio Signals ◽

Time Frequency ◽

Frequency Feature

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Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson's disease prediction

Computer Speech & Language ◽

10.1016/j.csl.2021.101216 ◽

2021 ◽

pp. 101216

Author(s):

Biswajit Karan ◽

Sitanshu Sekhar Sahu ◽

Juan Rafael Orozco-Arroyave ◽

Kartik Mahto

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Feature Extraction ◽

Matrix Factorization ◽

Disease Prediction ◽

Time Frequency ◽

Voice Signal ◽

Frequency Feature ◽

Non Negative Matrix Factorization

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Time-Frequency Feature Extraction from Residues of Voiced Phonemes

IEEE Latin America Transactions ◽

10.1109/tla.2004.1468648 ◽

2004 ◽

Vol 2 (3) ◽

pp. 168-173

Author(s):

E.V. de Payer

Keyword(s):

Feature Extraction ◽

Time Frequency ◽

Frequency Feature

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Optimizing Time-Frequency Feature Extraction and Channel Selection through Gradient Backpropagation to Improve Action Decoding based on Subthalamic Local Field Potentials

2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ◽

10.1109/embc44109.2020.9175885 ◽

2020 ◽

Author(s):

Thomas Martineau ◽

Shenghong He ◽

Ravi Vaidyanathan ◽

Peter Brown ◽

Huiling Tan

Keyword(s):

Feature Extraction ◽

Local Field ◽

Local Field Potentials ◽

Channel Selection ◽

Field Potentials ◽

Time Frequency ◽

Frequency Feature

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Time-frequency feature extraction from frequency-modulated sounds in a population of auditory cortical neurons

Acoustical Science and Technology ◽

10.1250/ast.29.36 ◽

2008 ◽

Vol 29 (1) ◽

pp. 36-40 ◽

Author(s):

Shunji Sugimoto ◽

Yuta Suzuki ◽

Hiroyuki Tanaka ◽

Michinori Kubota ◽

Junsei Horikawa

Keyword(s):

Feature Extraction ◽

Cortical Neurons ◽

Time Frequency ◽

Frequency Feature

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Time-Frequency Feature Extraction of Newborn EEG Seizure Using SVD-Based Techniques

EURASIP Journal on Advances in Signal Processing ◽

10.1155/s1110865704406167 ◽

2004 ◽

Vol 2004 (16) ◽

Author(s):

Hamid Hassanpour ◽

Mostefa Mesbah ◽

Boualem Boashash

Keyword(s):

Feature Extraction ◽

Time Frequency ◽

Newborn Eeg ◽

Frequency Feature

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Time–Frequency Feature Extraction of Acoustic Emission Signals in Aluminum Alloy MIG Welding Process Based on SST and PCA

IEEE Access ◽

10.1109/access.2019.2935117 ◽

2019 ◽

Vol 7 ◽

pp. 113988-113998 ◽

Author(s):

Kuanfang He ◽

Xuejun Li

Keyword(s):

Acoustic Emission ◽

Feature Extraction ◽

Aluminum Alloy ◽

Welding Process ◽

Mig Welding ◽

Time Frequency ◽

Frequency Feature

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A novel sparse filtering approach based on time-frequency feature extraction and softmax regression for intelligent fault diagnosis under different speeds

Journal of Central South University ◽

10.1007/s11771-019-4116-5 ◽

2019 ◽

Vol 26 (6) ◽

pp. 1607-1618 ◽

Author(s):

Zhong-wei Zhang ◽

Huai-hai Chen ◽

Shun-ming Li ◽

Jin-rui Wang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Intelligent Fault Diagnosis ◽

Softmax Regression ◽

Time Frequency ◽

Sparse Filtering ◽

Frequency Feature ◽

Filtering Approach

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Damage Classification for Structural Health Monitoring Using Time-Frequency Feature Extraction and Continuous Hidden Markov Models

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers ◽

10.1109/acssc.2007.4487337 ◽

2007 ◽

Author(s):

W. Zhou ◽

D. Chakraborty ◽

N. Kovvali ◽

A. Papandreou-Suppappola ◽

D. Cochran ◽

...

Keyword(s):

Feature Extraction ◽

Structural Health Monitoring ◽

Hidden Markov Models ◽

Health Monitoring ◽

Markov Models ◽

Hidden Markov ◽

Damage Classification ◽

Time Frequency ◽

Structural Health ◽

Frequency Feature

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Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection

Pattern Recognition ◽

10.1016/j.patcog.2014.08.016 ◽

2015 ◽

Vol 48 (3) ◽

pp. 616-627 ◽

Author(s):

Boualem Boashash ◽

Ghasem Azemi ◽

Nabeel Ali Khan

Keyword(s):

Feature Extraction ◽

Change Detection ◽

Abnormality Detection ◽

Time Frequency ◽

Stationary Signals ◽

Newborn Eeg ◽

Frequency Feature

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Time-Frequency Feature Extraction of Broadband Echo Signals from Individual Live Fish for Species Identification

Korean Journal of Fisheries and Aquatic Sciences ◽

10.5657/kfas.2016.0214 ◽

2016 ◽

Vol 49 (2) ◽

pp. 214-223

Author(s):

Dae-Jae Lee ◽

Hee-Young Kang ◽

Yong-Ye Pak

Keyword(s):

Feature Extraction ◽

Species Identification ◽

Time Frequency ◽

Live Fish ◽

Frequency Feature

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