scholarly journals SOME NEW RESULTS ON AUTOMATIC IDENTIFICATION OF VIETNAMESE FOLK SONGS CHEO AND QUANHO

2020 ◽  
Vol 36 (4) ◽  
pp. 325-345
Author(s):  
Chu Ba Thanh ◽  
Trinh Van Loan ◽  
Nguyen Hong Quang
Keyword(s):  
Gaussian Mixture ◽  
Folk Songs ◽  
Identification Accuracy ◽  
Automatic Identification ◽  
Gaussian Component ◽  
Mel Frequency Cepstral Coefficients ◽  
Identification Rate ◽  
First Derivative ◽  
Search For Information

  Vietnamese folk songs are very rich in genre and content. Identifying Vietnamese folk tunes will contribute to the storage and search for information about these tunes automatically. The paper will present an overview of the classification of music genres that have been performed in Vietnam and abroad. For two types of very popular folk songs of Vietnam such as Cheo and Quan ho, the paper describes the dataset and GMM (Gaussian Mixture Model) to perform the experiments on identifying some of these folk songs. The GMM used for experiment with 4 sets of parameters containing MFCC (Mel Frequency Cepstral Coefficients), energy, first derivative and second derivative of MFCC and energy, tempo, intensity, and fundamental frequency. The results showed that the parameters added to the MFCCs contributed significantly to the improvement of the identification accuracy with the appropriate values of Gaussian component number M. Our experiments also showed that, on average, the length of the excerpts was only 29.63% of the whole song for Cheo and 38.1% of the whole song for Quan ho, the identification rate was only 3.1% and 2.33% less than the whole song for Cheo and Quan ho respectively.

scholarly journals Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

2020 ◽  
Vol 18 (2) ◽  
pp. 782
Author(s):  
Musab T. S. Al-Kaltakchi ◽  
Haithem Abd Al-Raheem Taha ◽  
Mohanad Abd Shehab ◽  
Mohamed A.M. Abdullah
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Gaussian Mixture ◽  
Identification Accuracy ◽  
Identification System ◽  
Good Representation ◽  
Mel Frequency Cepstral Coefficients ◽  
Normalization Methods ◽  
Cepstral Coefficients

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

Two-Level Classification in Determining the Age and Gender Group of a Speaker

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Ergün Yücesoy
Keyword(s):  
Classification Accuracy ◽  
Age Groups ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Optimum Number ◽  
Mel Frequency Cepstral Coefficients ◽  
Age And Gender ◽  
Number Of Components ◽  
And Gender

In this study, the classification of the speakers according to age and gender was discussed. Age and gender classes were first examined separately, and then by combining these classes a classification with a total of 7 classes was made. Speech signals represented by Mel-Frequency Cepstral Coefficients (MFCC) and delta parameters were converted into Gaussian Mixture Model (GMM) mean supervectors and classified with a Support Vector Machine (SVM). While the GMM mean supervectors were formed according to the Maximum-a-posteriori (MAP) adaptive GMM-Universal Background Model (UBM) configuration, the number of components was changed from 16 to 512, and the optimum number of components was decided. Gender classification accuracy of the system developed using aGender dataset was measured as 99.02% for two classes and 92.58% for three classes and age group classification accuracy was measured as 67.03% for female and 63.79% for male. In the classification of age and gender classes together in one step, an accuracy of 61.46% was obtained. In the study, a two-level approach was proposed for classifying age and gender classes together. According to this approach, the speakers were first divided into three classes as child, male and female, then males and females were classified according to their age groups and thus a 7-class classification was realized. This two-level approach was increased the accuracy of the classification in all other cases except when 32-component GMMs were used. While the highest improvement of 2.45% was achieved with 64 component GMMs, an improvement of 0.79 was achieved with 256 component GMMs.

Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

2016 ◽  
Vol 25 (3) ◽  
pp. 387-399
Author(s):  
P. Mahesha ◽  
D.S. Vinod
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Speaker Recognition ◽  
Gaussian Mixture ◽  
Modeling Technique ◽  
Mel Frequency Cepstral Coefficients ◽  
Automatic Speaker Recognition ◽  
Word Repetition ◽  
Syllable Repetition

AbstractThe classification of dysfluencies is one of the important steps in objective measurement of stuttering disorder. In this work, the focus is on investigating the applicability of automatic speaker recognition (ASR) method for stuttering dysfluency recognition. The system designed for this particular task relies on the Gaussian mixture model (GMM), which is the most widely used probabilistic modeling technique in ASR. The GMM parameters are estimated from Mel frequency cepstral coefficients (MFCCs). This statistical speaker-modeling technique represents the fundamental characteristic sounds of speech signal. Using this model, we build a dysfluency recognizer that is capable of recognizing dysfluencies irrespective of a person as well as what is being said. The performance of the system is evaluated for different types of dysfluencies such as syllable repetition, word repetition, prolongation, and interjection using speech samples from the University College London Archive of Stuttered Speech (UCLASS).

scholarly journals Fourier Descriptors Based Expert Decision Classification of Plug Seedlings

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yanhu He ◽  
Rongyang Wang ◽  
Yanfeng Wang ◽  
Chuanyu Wu
Keyword(s):  
Expert System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Identification Accuracy ◽  
Support Vector ◽  
Fourier Descriptors ◽  
Identification Rate ◽  
Image Acquisition System ◽  
Seedling Leaf

To enable automatic transplantation of plug seedlings and improve identification accuracy, an algorithm to identify ideal seedling leaf sets based on Fourier descriptors is developed, and a classification method based on expert system is adopted to improve the identification rate of the plug seedlings. First, the image of the plug seedlings is captured by image acquisition system, followed by application of K-means clustering for image segmentation and binary processing and identification of the ideal seedling leaf set by Fourier descriptors. Then we obtain feature vectors, such as gray scale (R+B+G)/3, hue H, and rectangularity. After that the knowledge model of the plug seedlings is defined, and the inference engine based on knowledge is designed. Finally, the recognizing test is carried out. The success rate of the identification of 10 varieties of plug seedlings from 190 plates is 98.5%. For the same sample, the recognizing rate of support vector machine (SVM) is 85%, the recognizing rate of particle-swarm optimization SVM (PSOSVM) is 87%, the recognizing rate of back propagation neural network (BP) is 63%, and the recognizing rate of Fourier descriptors SVM (FDSVM) is 87%. These results show that our recognition method based on an expert system satisfies the requirements of automatic transplanting.

scholarly journals Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

2019 ◽  
Vol 9 (19) ◽  
pp. 4097 ◽  
Author(s):  
Juan J. Noda ◽  
Carlos M. Travieso-González ◽  
David Sánchez-Rodríguez ◽  
Jesús B. Alonso-Hernández
Keyword(s):  
Success Rate ◽  
Acoustic Signals ◽  
Species Level ◽  
Insect Species ◽  
Automatic Identification ◽  
Mel Frequency Cepstral Coefficients ◽  
New Approach ◽  
Cepstral Coefficients ◽  
Acoustic Classification

This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity. The study proposes a sound parameterization technique designed specifically for identification and classification of acoustic signals of insects using Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC). These two sets of coefficients are evaluated individually as has been done in previous studies and have been compared with the fusion proposed in this work, showing an outstanding increase in identification and classification at species level reaching a success rate of 98.07% on 343 insect species.

scholarly journals Middle Eastern and North African English Speech Corpus (MENAESC): Automatic Identification of MENA English Accents

2020 ◽  
Vol 18 (1) ◽  
Keyword(s):  
Arab World ◽  
Mother Tongue ◽  
Middle Eastern ◽  
Gaussian Mixture ◽  
Automatic Identification ◽  
North African ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Corpus ◽  
Arabic Speakers ◽  
System Effectiveness

This study aims to explore the English accents in the Arab world. Although there are limited resources for a speech corpus that attempts to automatically identify the degree of accent patterns of an Arabic speaker of English, there is no speech corpus specialized for Arabic speakers of English in the Middle East and North Africa (MENA). To that end, different samples were collected in order to create the linguistic resource that we called Middle Eastern and North African English Speech Corpus (MENAESC). In addition to the “accent approach” applied in the field of automatic language/dialect recognition; we applied also the “macro-accent approach” -by employing Mel-Frequency Cepstral Coefficients (MFCC), Energy and Shifted Delta Cepstra (SDC) features and Gaussian Mixture Model-Universal Background Model (GMM-UBM) classifier- on four accents (Egyptian, Qatari, Syrian, and Tunisian accents) among the eleven accents that were selected based on their high population density in the location where the experiments were carried out. By using the Equal Error Rate percentage (EER%) for the assessment of our system effectiveness in the identification of MENA English accents using the two approaches mentioned above through the employ of the MENAESC, results showed we reached 1.5 to 2%, for “accent approach” and 2 to 3.5% for “macro-accents approach” for identification of MENA English. It also exhibited that the Qatari accent, of the 4 accents included, scored the lowest EER% for all tests performed. Taken together, the system effectiveness is not only affected by the approaches used, but also by the database size MENAESC and its characteristics. Moreover, it is impacted by the proficiency of the Arabic speakers of English and the influence of their mother tongue

Generating realistic Arabic handwriting dataset

2019 ◽  
Vol 8 (4) ◽  
pp. 460
Author(s):  
Mahmoud I. Abdalla ◽  
Mohsen A. Rashwan ◽  
Mohamed A. Elserafy
Keyword(s):  
Word Recognition ◽  
Word List ◽  
Holistic Approach ◽  
Gaussian Mixture ◽  
Normalization Method ◽  
Gaussian Component ◽  
Efficient System ◽  
Mixture Regression ◽  
Word Generation ◽  
Input Text

During the previous year's holistic approach showing satisfactory results to solve ‎the ‎problem of Arabic handwriting word  recognition instead of word letters ‎‎segmentation.‎ ‎In this paper, we present an efficient system for ‎ generation realistic Arabic handwriting dataset from ASCII input ‎text. We carefully selected simple word list that contains most Arabic ‎letters normal and ligature connection cases. To improve the ‎performance of new letters reproduction we developed our ‎normalization method that adapt its clustering action according to ‎created Arabic letters families. We enhanced  Gaussian Mixture ‎Model process to learn letters template by detecting the ‎number and position of Gaussian component by implementing ‎Ramer-Douglas-Peucker‎ algorithm which improve the new letters ‎shapes reproduced by using and Gaussian Mixture Regression. ‎‎We learn the translation distance between word-part to achieve ‎real handwriting word generation shape.‎ Using combination of LSTM and CTC layer as a recognizer to validate the ‎efficiency of our approach in generating new realistic Arabic handwriting words inherit user handwriting style as shown by the experimental results.‎ 

Strategies to Enhance the Performance of Gaussian Mixture Model Fitting for Uncertainty Quantification by Conditioning to Production Data

2021 ◽  
Author(s):  
Guohua Gao ◽  
Jeroen Vink ◽  
Fredrik Saaf ◽  
Terence Wells
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
History Matching ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Gaussian Component ◽  
Large Set ◽  
Cpu Time ◽  
Fitting Method ◽  
New Strategies

Abstract When formulating history matching within the Bayesian framework, we may quantify the uncertainty of model parameters and production forecasts using conditional realizations sampled from the posterior probability density function (PDF). It is quite challenging to sample such a posterior PDF. Some methods e.g., Markov chain Monte Carlo (MCMC), are very expensive (e.g., MCMC) while others are cheaper but may generate biased samples. In this paper, we propose an unconstrained Gaussian Mixture Model (GMM) fitting method to approximate the posterior PDF and investigate new strategies to further enhance its performance. To reduce the CPU time of handling bound constraints, we reformulate the GMM fitting formulation such that an unconstrained optimization algorithm can be applied to find the optimal solution of unknown GMM parameters. To obtain a sufficiently accurate GMM approximation with the lowest number of Gaussian components, we generate random initial guesses, remove components with very small or very large mixture weights after each GMM fitting iteration and prevent their reappearance using a dedicated filter. To prevent overfitting, we only add a new Gaussian component if the quality of the GMM approximation on a (large) set of blind-test data sufficiently improves. The unconstrained GMM fitting method with the new strategies proposed in this paper is validated using nonlinear toy problems and then applied to a synthetic history matching example. It can construct a GMM approximation of the posterior PDF that is comparable to the MCMC method, and it is significantly more efficient than the constrained GMM fitting formulation, e.g., reducing the CPU time by a factor of 800 to 7300 for problems we tested, which makes it quite attractive for large scale history matching problems.

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