scholarly journals O USO DE REDES NEURAIS AUTO- ORGANIZÁVEIS NA VISUALIZAÇÃO DE FORMAÇÃO DE AGRUPAMENTOS A PARTIR DO CONHECIMENTO ACENTUAL DE APRENDIZES BRASILEIROS DE INGLÊS

Organon ◽  
2011 ◽  
Vol 26 (51) ◽  
Author(s):  
Ana Cristina Cunha
Keyword(s):  
Neural Network ◽  
Speech Signal ◽  
Early Stage ◽  
Predictive Coding ◽  
Lexical Stress ◽  
Self Organizing Map ◽  
L2 Acquisition ◽  
Linear Predictive Coding ◽  
Classi Cation ◽  
Basic Hypothesis

! is study aimed at investigating how Brazilian learners ofEnglish organize their knowledge about lexical stress of a speci" c wordcategory at an early stage of L2 acquisition with the help of an unsuper-vised neural network, a self-organizing map (SOM), also called Kohonennetwork. ! e basic hypothesis tested was whether the parameterization ofthe speech signal from learner’s utterances through processing techniquessuch as Linear Predictive Coding (LPC), which consisted of the input ofthe network, would be e# ective in the classi" cation of learners and theirutterances. ! e study consisted of an empirical part and a computationalone. ! e participants were beginner students aged between 18 and 25.Preliminary results indicate that the combination of LPC+SOM allowedthe creation of well-de" ned category clusters, which is an important stepin data classi" cation to aid language pro" ciency level determination, andcomputer-assisted pronunciation teaching.

Multi-Rider Optimization-Based Neural Network for Fault Isolation in Analog Circuits

2020 ◽  
pp. 2150048
Author(s):  
D. Binu ◽  
B. S. Kariyappa
Keyword(s):  
Neural Network ◽  
Analog Circuits ◽  
Minimum Mean Square Error ◽  
Predictive Coding ◽  
Principal Component ◽  
Power Converter ◽  
Fault Isolation ◽  
Linear Predictive Coding ◽  
Fault Dictionary ◽  
Area Of Interest

Fault isolation in electronic circuits is a trending area of interest as analog circuits find valuable application in industry. The failures in circuit systems cause severe issues in the normal functioning of the system that insists on the need for an automatic method of fault isolation in analog circuits. Literature conveys the issues associated with the fault isolation and hence, to address the severity of the faults, a novel model is proposed to isolate the fault causing component in the circuit. The proposed Multi-Rider Optimization-based Neural Network (M-RideNN) isolates the faulty part of the circuit from the fault-free areas such that the fault diagnosis is structured in an effective way. The fault isolation is progressed as four major steps such as establishing the fault dictionary, signal normalization using Linear Predictive Coding (LPC), effective dimensional reduction methodology using Probabilistic Principal Component Analysis (PPCA), and fault isolation using the proposed M-RideNN classifier. Finally, the experimentation using three circuits, namely Triangular Wave Generator (TWG), Bipolar Transistor Amplifier (BTA), differentiator (DIF), and an application circuit, Solar Power Converter (SPC), proves that the proposed M-RideNN classifier offers better classification accuracy of 93.18% with a minimum Mean Square Error (MSE) of 0.0682.

scholarly journals On Bubble Sizing in Water by Ultrasound

2017 ◽  
Vol 11 (2) ◽  
pp. 128
Author(s):  
Walid B. Hussein ◽  
Sarah A. Essmat ◽  
Nestor Yoma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Coding ◽  
Air Bubbles ◽  
Linear Predictive Coding ◽  
Envelope Detector ◽  
Ultrasound Field ◽  
Crucial Problem ◽  
Bubble Sizes ◽  
Bubble Sizing

<p class="Abstract">Classifying bubbles in liquids is a crucial problem that is demanded within multiple fields. This paper discusses a new method for classifying bubble sizes in non-contact and inexpensive approach using ultrasound analysis. Exploiting the principle of buoyancy, free rising bubbles with larger volumes elevate faster to the surface compared to the smaller ones, given that they have the same densities. An envelope detector is proposed which tracks the changes in the ultrasound signals reflected by the bubbles when they cross the ultrasound field. These changes in the reflected signals are distinctive for the sizes under consideration. Relevant spectral and linear predictive coding features that represent the distinct characteristics are extracted. These features are fed to a feed-forward artificial neural network to successfully classify air bubbles according to their sizes with an accuracy of 98.8%. This method provides promising applications to be implemented in industrial, biomedical and environmental fields.</p>

Speech Recognition Using Elman Artificial Neural Network and Linear Predictive Coding

2020 ◽  
Vol 13 (4) ◽  
pp. 650-656
Author(s):  
Somayeh Khajehasani ◽  
Louiza Dehyadegari
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Intelligent System ◽  
Recognition Accuracy ◽  
Predictive Coding ◽  
Recognition System ◽  
Visual Methods ◽  
Linear Predictive Coding ◽  
Elman Neural Network ◽  
Human Speech

Background: Today, the automatic intelligent system requirement has caused an increasing consideration on the interactive modern techniques between human being and machine. These techniques generally consist of two types: audio and visual methods. Meanwhile, the need for developing the algorithms that enable the human speech recognition by machine is of high importance and frequently studied by the researchers. Objective: Using artificial intelligence methods has led to better results in human speech recognition, but the basic problem is the lack of an appropriate strategy to select the recognition data among the huge amount of speech information that practically makes it impossible for the available algorithms to work. Method: In this article, to solve the problem, the linear predictive coding coefficients extraction method is used to sum up the data related to the English digits pronunciation. After extracting the database, it is utilized to an Elman neural network to recognize the relation between the linear coding coefficients of an audio file with the pronounced digit. Results: The results show that this method has a good performance compared to other methods. According to the experiments, the obtained results of network training (99% recognition accuracy) indicate that the network still has better performance than RBF despite many errors. Conclusion: The results of the experiments showed that the Elman memory neural network has had an acceptable performance in recognizing the speech signal compared to the other algorithms. The use of the linear predictive coding coefficients along with the Elman neural network has led to higher recognition accuracy and improved the speech recognition system.

scholarly journals Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

2022 ◽  
Vol 12 (2) ◽  
pp. 1982
Author(s):  
Nsiri Benayad ◽  
Zayrit Soumaya ◽  
Belhoussine Drissi Taoufiq ◽  
Ammoumou Abdelkrim
Keyword(s):  
Genetic Algorithm ◽  
Speech Signal ◽  
Wavelet Transforms ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Features Selection ◽  
Nearest Neighbour ◽  
Algorithm Optimization ◽  
Linear Predictive Coding ◽  
Zero Crossing

<span lang="EN-US">Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a <a name="_Hlk88480766"></a>linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification.</span>

scholarly journals Word recognition from speech signal using linear predictive coding and spectrum analysis

2018 ◽  
Vol 7 (3) ◽  
pp. 1531
Author(s):  
Mandeep Singh ◽  
Gurpreet Singh
Keyword(s):  
Word Recognition ◽  
Sample Size ◽  
Spectrum Analysis ◽  
Speech Signal ◽  
Predictive Coding ◽  
The Other ◽  
Hybrid Technique ◽  
Linear Predictive Coding ◽  
Statistical Parameters ◽  
Isolated Word

This paper presents a technique for isolated word recognition from speech signal using Spectrum Analysis and Linear Predictive Coding (LPC). In the present study, only those words have been analyzed which are commonly used during a telephonic conversations by criminals. Since each word is characterized by unique frequency spectrum signature, thus, spectrum analysis of a speech signal has been done using certain statistical parameters. These parameters help in recognizing a particular word from a speech signal, as there is a unique value of a feature for each word, which helps in distinguishing one word from the other. Second method used is based on LPC coefficients. Analysis of features extracted using LPC coefficients help in identification of a specific word from the input speech signal. Finally, a combination of best features from these two methods has been used and a hybrid technique is proposed. An accuracy of 94% has been achieved for sample size of 400 speech words.  

scholarly journals Neural analysis of seismic data: applications to the monitoring of Mt. Vesuvius

2013 ◽  
Vol 56 (4) ◽  
Author(s):  
Antonietta M. Esposito ◽  
Luca D’Auria ◽  
Flora Giudicepietro ◽  
Teresa Caputo ◽  
Marcello Martini
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Intelligent Systems ◽  
Predictive Coding ◽  
Automatic Analysis ◽  
Seismic Signals ◽  
Self Organizing Map ◽  
Linear Predictive Coding ◽  
Advanced Analysis

<p>The computing techniques currently available for the seismic monitoring allow advanced analysis. However, the correct event classification remains a critical aspect for the reliability of real time automatic analysis. Among the existing methods, neural networks may be considered efficient tools for detection and discrimination, and may be integrated into intelligent systems for the automatic classification of seismic events. In this work we apply an unsupervised technique for analysis and classification of seismic signals recorded in the Mt. Vesuvius area in order to improve the automatic event detection. The examined dataset contains about 1500 records divided into four typologies of events: earthquakes, landslides, artificial explosions, and “other” (any other signals not included in the previous classes). First, the Linear Predictive Coding (LPC) and a waveform parametrization have been applied to achieve a significant and compact data encoding. Then, the clustering is obtained using a Self-Organizing Map (SOM) neural network which does not require an a-priori classification of the seismic signals, groups those with similar structures, providing a simple framework for understanding the relationships between them. The resulting SOM map is separated into different areas, each one containing the events of a defined type. This means that the SOM discriminates well the four classes of seismic signals. Moreover, the system will classify a new input pattern depending on its position on the SOM map. The proposed approach can be an efficient instrument for the real time automatic analysis of seismic data, especially in the case of possible volcanic unrest.</p>

Pembangunan Satu Kaedah Cekap dalam Pengesanan dan Pengkelasan Gangguan Kualiti Kuasa

2012 ◽  
Author(s):  
Ramizi Mohamed ◽  
Azah Mohamed ◽  
Aini Hussain
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Linear Predictive Coding ◽  
Power Quality Disturbance ◽  
Voltage Swell ◽  
Power Quality Disturbances

Pengesanan dan pengkelasan data gangguan kualiti kuasa secara automatik telah menjadi penting terutamanya untuk menangani masalah gangguan pangkalan data yang besar. Kertas kerja ini membentangkan satu kaedah cekap dalam pengesanan dan pengkelasan gangguan kualiti kuasa. Kaedah yang dicadangkan untuk mengesan gangguan adalah berdasarkan penjelmaan anak gelombang diskrit dan pengekodan ramalan lelurus manakala kaedah yang telah dibangunkan untuk mengkelaskan gangguan adalah berdasarkan rangkaian neural tiruan (RNT). Sebelum pelaksaan RNT, isyarat gangguan dikesan terlebih dahulu untuk mendapatkan pekali anak gelombang kuasa dua dan pekali pengekodan ramalan lelurus. Pekali ini mewakili sifat bagi berbagai jenis gangguan dan digunakan sebagai data masukan kepada RNT yang telah dibina. Oleh itu, anak gelombang dan pengekodan ramalan lelurus digunakan sebagai prapemprosesan isyarat gangguan yang kemudiannya disambungkan kepada RNT. Dalam pelaksanan RNT, model rangkaian neural lapisan berbilang dengan algoritma perambatan ke belakang telah dipertimbangkan. Reka bentuk RNT yang telah dibangunkan adalah berbentuk hierarki dan modular supaya RNT yang berasingan dikhaskan untuk mengkelas berbagai jenis gangguan dan juga gangguan dengan kadar persampelan yang berbeza. Keputusan yang diperolehi menunjukkan bahawa kaedah anak gelombang dan pengekodan ramalan lelurus adalah sangat berkesan untuk mengesan gangguan kualiti kuasa dan kaedah RNT pula dapat mengkelaskan dengan jitu gangguan kualiti kuasa seperti lendut voltan, ampul voltan, fana dan takukan. Kata kunci: Kualiti kuasa; anak gelombang; pengekodan ramalan lelurus; rangkaian neural Automated power quality disturbance detection and classification is preferred so as to enable faster and more efficient analysis of a disturbance large database. This paper presents an efficient method to detect and classify some power quality disturbances. The proposed method for detecting the disturbances is based on discrete wavelet transform and linear predictive coding whereas the method for classifying the disturbances is based on artificial neural network (ANN). Prior to the ANN implementation, the disturbance signals are first detected by the discrete wavelet transform and the linear predictive coding techniques to obtain the squared wavelet transform coefficients and the linear predictive coding coefficients. These features represent the various disturbances and serve as inputs to the developed ANNs. Therefore, wavelets and linear predictive coding are employed as a preprocessing stage and is connected to the ANN. In the ANN implementation, the multilayer perceptron neural network model and the backpropagation algorithm are considered. The design of the developed ANNs are hierarchical as well as modular in nature so that separate ANNs are dedicated to classify the various types of disturbances and to handle the disturbances with different sampling rates. The results obtained show that the wavelets and the linear predictive coding methods are effective in detecting power quality disturbances and the ANNs can accurately classify the disturbances such as voltage sag, voltage swell, transients and notching. Key words: Power quality; wavelets; linear predictive coding; neural networks

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