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.

AUTOMATED RECOGNITION OF SINGLE & HYBRID POWER QUALITY DISTURBANCES USING WAVELET TRANSFORM BASED SUPPORT VECTOR MACHINE

2016 ◽  
Vol 79 (1) ◽  
Author(s):  
Suhail Khokhar ◽  
A. A. Mohd Zin ◽  
M. A. Bhayo ◽  
A. S. Mokhtar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network

The monitoring of power quality (PQ) disturbances in a systematic and automated way is an important issue to prevent detrimental effects on power system. The development of new methods for the automatic recognition of single and hybrid PQ disturbances is at present a major concern. This paper presents a combined approach of wavelet transform based support vector machine (WT-SVM) for the automatic classification of single and hybrid PQ disturbances. The proposed approach is applied by using synthetic models of various single and hybrid PQ signals. The suitable features of the PQ waveforms were first extracted by using discrete wavelet transform. Then SVM classifies the type of PQ disturbances based on these features. The classification performance of the proposed algorithm is also compared with wavelet based radial basis function neural network, probabilistic neural network and feed-forward neural network. The experimental results show that the recognition rate of the proposed WT-SVM based classification system is more accurate and much better than the other classifiers. 

scholarly journals The Use of Arabic Vowels to Model the Pathological Effect of Influenza Disease by Wavelets

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Khaled Daqrouq ◽  
Abdel-Rahman Al-Qawasmi ◽  
Ahmed Balamesh ◽  
Ali S. Alghamdi ◽  
Mohamed A. Al-Amoudi
Keyword(s):  
Linear Prediction ◽  
Nearest Neighbor ◽  
Vocal Tract ◽  
Characteristic Curve ◽  
Voice Disorder ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Pathological Effect ◽  
Power Spectral ◽  
Linear Prediction Coding

Speech parameters may include perturbation measurements, spectral and cepstral modeling, and pathological effects of some diseases, like influenza, that affect the vocal tract. The verification task is a very good process to discriminate between different types of voice disorder. This study investigated the modeling of influenza’s pathological effects on the speech signals of the Arabic vowels “A” and “O.” For feature extraction, linear prediction coding (LPC) of discrete wavelet transform (DWT) subsignals denoted by LPCW was used. k-Nearest neighbor (KNN) and support vector machine (SVM) classifiers were used for classification. To study the pathological effects of influenza on the vowel “A” and vowel “O,” power spectral density (PSD) and spectrogram were illustrated, where the PSD of “A” and “O” was repressed as a result of the pathological effects. The obtained results showed that the verification parameters achieved for the vowel “A” were better than those for vowel “O” for both KNN and SVM for an average. The receiver operating characteristic curve was used for interpretation. The modeling by the speech utterances as words was also investigated. We can claim that the speech utterances as words could model the influenza disease with a good quality of the verification parameters with slightly less performance than the vowels “A” as speech utterances. A comparison with state-of-the-art method was made. The best results were achieved by the LPCW method.

A WAVELET-BASED SPEAKER VERIFICATION ALGORITHM

2010 ◽  
Vol 08 (06) ◽  
pp. 905-912 ◽  
Author(s):  
MICHEL ALVES LACERDA ◽  
RODRIGO CAPOBIANCO GUIDO ◽  
LEONARDO MENDES DE SOUZA ◽  
PAULO RICARDO FRANCHI ZULATO ◽  
JUSSARA RIBEIRO ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transforms ◽  
Finite Impulse Response ◽  
Speaker Verification ◽  
Fir Filters ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Verification Algorithm ◽  
Support Size ◽  
Artificial Neural Network Ann

This paper presents a study on wavelets and their characteristics for the specific purpose of serving as a feature extraction tool for speaker verification (SV), considering a Radial Basis Function (RBF) classifier, which is a particular type of Artificial Neural Network (ANN). Examining characteristics such as support-size, frequency and phase responses, amongst others, we show how Discrete Wavelet Transforms (DWTs), particularly the ones which derive from Finite Impulse Response (FIR) filters, can be used to extract important features from a speech signal which are useful for SV. Lastly, an SV algorithm based on the concepts presented is described.

scholarly journals Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

2015 ◽  
Vol 5 (1) ◽  
pp. 92 ◽  
Author(s):  
A. B. M. Aowlad Hossain ◽  
Md. Wasiur Rahman ◽  
Manjurul Ahsan Riheen
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Hand Movement ◽  
Hand Movements ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Beta Band ◽  
Right Hand ◽  
Left And Right

Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features.  The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications.

scholarly journals Penentuan Notasi Gamelan Rindik Menggunakan Metode Transformasi Wavelet

2018 ◽  
Vol 17 (3) ◽  
pp. 319
Author(s):  
I Gusti Made Meri Utama Yasa ◽  
Linawati Linawati ◽  
N Paramaita
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Wavelet Function ◽  
Discrete Wavelet ◽  
Daubechies Wavelet ◽  
Data Process ◽  
Average Accuracy ◽  
Accuracy Level

Abstract—This paper present about recognition of gamelan rindik pattern using wavelet transform. Wavelet transform is used to find the special characteristic of gamelan rindik, which had previously been recorded and stored in computer with format *.wav. The data was subsequently used as training and tested data, Probabilistic Neural Network (PNN) was used to recognize gamelan rindik pattern using. The training and tasted data process used four different rindics, consisting 0f 240 gamelan rindik data. Discrete Wavelet Transform (DWT) was used as the method of feature extraction, with Symlet, Haar, and Daubechies Wavelet function. Those three functions of the wavelet  shows the average accuracy level for Symlet 94.58%, Haar 93.33%, and wavelet Daubechies 94.58%.

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