Neural Networks and Linear Predictive Coding Coefficients Used for European Starling Detection in Vineyards

2015 ◽  
pp. 91-99
Author(s):  
Petr Dolezel ◽  
Martin Mariska
Keyword(s):  
Neural Networks ◽  
Predictive Coding ◽  
European Starling ◽  
Linear Predictive Coding

CLASSIFICATION OF VENTRICULAR ECTOPIC BEATS (VEB'S) USING NEURAL NETWORKS

2004 ◽  
Vol 04 (03) ◽  
pp. 333-340 ◽  
Author(s):  
M. A. CHIKH ◽  
F. BEREKSI–REGUIG
Keyword(s):  
Neural Networks ◽  
Predictive Coding ◽  
Temporal Distribution ◽  
Principal Component ◽  
Clinical Use ◽  
Linear Predictive Coding ◽  
Ectopic Beats ◽  
Neural Network Classifiers ◽  
Electrocardiogram Ecg

The most widely used signal in clinical practice is the electrocardiogram (ECG). ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important parameters measured from the temporal distribution of the ECG constituent waves. The purpose of this paper is the classification of ventricular ectopic beats (VEB's). This research includes noise handling, feature extraction, and neural classification, all integrated in a three-stage procedure. Thirty features extracted from the morphology of the QRS segment, are reduced to seven coefficients using principal component analysis (PCA) and two coefficients using linear predictive coding (LPC) technique in addition to two other temporal parameters were used separately as the input of two neural network classifiers. The neural classifiers were tested on the MIT-BIH database and high scores were obtained for both sensitivity and specificity (84.88% and 91.92% respectively using ACP technique, and 76.17% and 88.95% using LPC method). This study confirms the power of artificial neural networks in the classification of normal and abnormal VEB beats. Clinical use of this method, however, still requires further investigation.

Exploring vowel formant estimation through simulation-based techniques

2020 ◽  
Vol 6 (s1) ◽  
Author(s):  
Tyler Kendall ◽  
Charlotte Vaughn
Keyword(s):  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Fine Grained ◽  
Vowel Formant ◽  
Simulation Based ◽  
Insight Into

AbstractThis paper contributes insight into the sources of variability in vowel formant estimation, a major analytic activity in sociophonetics, by reviewing the outcomes of two simulations that manipulated the settings used for linear predictive coding (LPC)-based vowel formant estimation. Simulation 1 explores the range of frequency differences obtained when minor adjustments are made to LPC settings, and measurement timepoints around the settings used by trained analysts, in order to determine the range of variability that should be expected in sociophonetic vowel studies. Simulation 2 examines the variability that emerges when LPC settings are varied combinatorially around constant default settings, rather than settings set by trained analysts. The impacts of different LPC settings are discussed as a way of demonstrating the inherent properties of LPC-based formant estimation. This work suggests that differences more fine-grained than about 10 Hz in F1 and 15–20 Hz in F2 are within the range of LPC-based formant estimation variability.

Power Efficient Speaker Verification Using Linear Predictive Coding on FPGA

2018 ◽  
Author(s):  
Amit Sravan Bora ◽  
Rohit Reddy ◽  
Srinath Satpathy ◽  
H. Balachander ◽  
V. Vijendra ◽  
...  
Keyword(s):  
Speaker Verification ◽  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Power Efficient

scholarly journals Navigation Security Module with Real-Time Voice Command Recognition System

2017 ◽  
Vol 24 (2) ◽  
pp. 17-26
Author(s):  
Mustafa Yagimli ◽  
Huseyin Kursat Tezer
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Predictive Coding ◽  
Recognition System ◽  
Time Warping ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Voice Command ◽  
Security Module ◽  
Dynamic Time

Abstract The real-time voice command recognition system used for this study, aims to increase the situational awareness, therefore the safety of navigation, related especially to the close manoeuvres of warships, and the courses of commercial vessels in narrow waters. The developed system, the safety of navigation that has become especially important in precision manoeuvres, has become controllable with voice command recognition-based software. The system was observed to work with 90.6% accuracy using Mel Frequency Cepstral Coefficients (MFCC) and Dynamic Time Warping (DTW) parameters and with 85.5% accuracy using Linear Predictive Coding (LPC) and DTW parameters.

scholarly journals Residual Excited Linear Predictive Coding

2015 ◽  
Vol V4 (05) ◽  
Author(s):  
Santosh F Bankar ◽  
N Kolhare ◽  
Keyword(s):  
Predictive Coding ◽  
Linear Predictive Coding
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