New results on the estimation and tracking of the frequencies of sinusoidal signals in noise by use of linear prediction

1978 ◽  
Vol 63 (S1) ◽  
pp. S49-S49
Author(s):  
D. W. Tufts ◽  
R. M. Rao ◽  
R. Kumaresan ◽  
L. B. Jackson
Keyword(s):  
Linear Prediction ◽  
Sinusoidal Signals

scholarly journals Application of parametric modelling methods for estimating the parameters of damped sinusoidal signals

2017 ◽  
Vol 66 (4) ◽  
pp. 193-208
Author(s):  
Piotr Figoń
Keyword(s):  
Linear Prediction ◽  
Analytical Signal ◽  
Parametric Modelling ◽  
Deterministic Component ◽  
Sinusoidal Waveform ◽  
Sinusoidal Signals ◽  
Modelling Methods

The paper describes the results of analyses aimed at obtaining information on selected parameters of the deterministic component of a signal being the sum of two waveforms, an exponentially damped sinusoidal waveform and a stochastic waveform. Keywords: linear prediction, optimisation, analytical signal

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

scholarly journals On the Speech Properties and Feature Extraction Methods in Speech Emotion Recognition

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1888
Author(s):  
Juraj Kacur ◽  
Boris Puterka ◽  
Jarmila Pavlovicova ◽  
Milos Oravec
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Filter Banks ◽  
Vocal Tract ◽  
Statistical Tests ◽  
Extraction Methods ◽  
Speech Emotion Recognition ◽  
Speech Characteristics ◽  
Evaluation Phase ◽  
Cepstral Features

Many speech emotion recognition systems have been designed using different features and classification methods. Still, there is a lack of knowledge and reasoning regarding the underlying speech characteristics and processing, i.e., how basic characteristics, methods, and settings affect the accuracy, to what extent, etc. This study is to extend physical perspective on speech emotion recognition by analyzing basic speech characteristics and modeling methods, e.g., time characteristics (segmentation, window types, and classification regions—lengths and overlaps), frequency ranges, frequency scales, processing of whole speech (spectrograms), vocal tract (filter banks, linear prediction coefficient (LPC) modeling), and excitation (inverse LPC filtering) signals, magnitude and phase manipulations, cepstral features, etc. In the evaluation phase the state-of-the-art classification method and rigorous statistical tests were applied, namely N-fold cross validation, paired t-test, rank, and Pearson correlations. The results revealed several settings in a 75% accuracy range (seven emotions). The most successful methods were based on vocal tract features using psychoacoustic filter banks covering the 0–8 kHz frequency range. Well scoring are also spectrograms carrying vocal tract and excitation information. It was found that even basic processing like pre-emphasis, segmentation, magnitude modifications, etc., can dramatically affect the results. Most findings are robust by exhibiting strong correlations across tested databases.

Effect of respiratory muscle tension on lung volume

1992 ◽  
Vol 73 (6) ◽  
pp. 2283-2288 ◽  
Author(s):  
T. A. Wilson ◽  
A. De Troyer
Keyword(s):  
Chest Wall ◽  
Lung Volume ◽  
Airway Pressure ◽  
Linear Prediction ◽  
Muscle Tension ◽  
Muscle Length ◽  
Respiratory Muscles ◽  
Active Tension ◽  
Airway Opening ◽  
Measured Change

The chest wall is modeled as a linear system for which the displacements of points on the chest wall are proportional to the forces that act on the chest wall, namely, airway opening pressure and active tension in the respiratory muscles. A standard theorem of mechanics, the Maxwell reciprocity theorem, is invoked to show that the effect of active muscle tension on lung volume, or airway pressure if the airway is closed, is proportional to the change of muscle length in the relaxation maneuver. This relation was tested experimentally. The shortening of the cranial-caudal distance between a rib pair and the sternum was measured during a relaxation maneuver. These data were used to predict the respiratory effect of forces applied to the ribs and sternum. To test this prediction, a cranial force was applied to the rib pair and a caudal force was applied to the sternum, simulating the forces applied by active tension in the parasternal intercostal muscles. The change in airway pressure, with lung volume held constant, was measured. The measured change in airway pressure agreed well with the prediction. In some dogs, nonlinear deviations from the linear prediction occurred at higher loads. The model and the theorem offer the promise that existing data on the configuration of the chest wall during the relaxation maneuver can be used to compute the mechanical advantage of the respiratory muscles.

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