Analysis of Infant Cry Through Weighted Linear Prediction Cepstral Coefficients and Probabilistic Neural Network

2010 ◽  
Vol 36 (3) ◽  
pp. 1309-1315 ◽  
Author(s):  
M. Hariharan ◽  
Lim Sin Chee ◽  
Sazali Yaacob
Keyword(s):  
Neural Network ◽  
Linear Prediction ◽  
Probabilistic Neural Network ◽  
Infant Cry ◽  
Linear Prediction Cepstral Coefficients ◽  
Cepstral Coefficients

scholarly journals Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Mousmita Sarma ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Linear Prediction ◽  
Vocal Tract ◽  
Probabilistic Neural Network ◽  
Recognition Performance ◽  
Spoken Word Recognition ◽  
Discrete Wavelet ◽  
Self Organizing Map ◽  
Formant Frequency ◽  
Artificial Neural Network Ann

In spoken word recognition, one of the crucial points is to identify the vowel phonemes. This paper describes an Artificial Neural Network (ANN) based algorithm developed for the segmentation and recognition of the vowel phonemes of Assamese language from some words containing those vowels. Self-Organizing Map (SOM) trained with a various number of iterations is used to segment the word into its constituent phonemes. Later, Probabilistic Neural Network (PNN) trained with clean vowel phonemes is used to recognize the vowel segment from the six different SOM segmented phonemes. One of the important aspects of the proposed algorithm is that it proves the validation of the recognized vowel by checking its first formant frequency. The first formant frequency of all the Assamese vowels is predetermined by estimating pole or formant location from the linear prediction (LP) model of the vocal tract. The proposed algorithm shows a high recognition performance in comparison to the conventional Discrete Wavelet Transform (DWT) based segmentation.

ANALYZING INFANT CRIES USING A COMMITTEE OF NEURAL NETWORKS IN ORDER TO DETECT HYPOXIA RELATED DISORDER

2006 ◽  
Vol 15 (03) ◽  
pp. 397-410 ◽  
Author(s):  
MANNES POEL ◽  
TACO EKKEL
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Small Data ◽  
Data Set ◽  
Related Disorder ◽  
Linear Discriminant ◽  
Infant Cry ◽  
Classifier System

Based on the hypothesis that the sound of the infant cry contains information on the infant's health status, research has been done on how to improve classification of neonate crying sounds into categories called 'normal' and 'abnormal' - the latter referring to some hypoxia-related disorder. Research in this field is hindered by lack of test cases and limited understanding of feature relevance. The research described here combines various ways of dealing with the small data set problem. First, feature pre-selection is done using sequential backwards elimination of possible combinations where the performance of the set of features is tested by a Probabilistic Neural Network which has the advantage of fast learning. Using these features a neural network committee, consisting of Radial Basis Function Neural Networks, was trained on the data, using bootstrapping. This construction yields a multi-classifier system with an overall classification performance of 85% on the so-called "All Cry Units" (ACU) data set, an increase of 34% with respect to the a priori probability of 51%. Several leave-1-out experiments for Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and Neural Networks (NN) have been conducted in order to compare the performance of the multi-classifier system.

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

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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