On the periodicity of speech coded with linear-prediction based analysis by synthesis coders

1994 ◽  
Vol 2 (4) ◽  
pp. 539-542 ◽  
Author(s):  
W.B. Kleijn
Keyword(s):  
Linear Prediction ◽  
Analysis By Synthesis

scholarly journals Fixed-Location Pulse Linear Prediction Coding Vocoder Model for Low Bit Rate Speech Coding

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Zhen Ma
Keyword(s):  
Linear Prediction ◽  
Theoretical Foundation ◽  
Least Square ◽  
Low Bit Rate ◽  
Synthesized Speech ◽  
Analysis By Synthesis ◽  
Arbitrary Location ◽  
Linear Prediction Coding ◽  
Prediction Coding

An arbitrary-location pulse determination algorithm based on multipulse linear prediction coding (MP-LPC) is presented. This algorithm can determine all the amplitudes of the pulses at a time according to given pulse locations without the use of analysis-by-synthesis. This ensures that the pulses are optimal in a least-square sense, providing the theoretical foundation to improve the quality of synthesized speech. A fixed-location pulse linear prediction coding (FLP-LPC) method is proposed based on the arbitrary-location pulse determination algorithm. Simulation of the algorithm in MATLAB showed the superior quality of the speech synthesized using pulses in different locations and processed using the arbitrary-location pulse determination algorithm. The algorithm improved speech quality without affecting coding time, which was approximately 1.5% of the coding time for MP-LPC. Pulse locations in FLP-LPC are fixed and do not need to be transmitted, with only LSF, gain, and 16 pulse amplitudes requiring coding and transmission. FLP-LPC allows the generation of synthesized speech similar to G.729 coded speech at a rate of 2.5 kbps.

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

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