scholarly journals Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

2021 ◽  
Vol 13 (2) ◽  
pp. 84-93
Author(s):  
Heriyanto Heriyanto ◽  
Tenia Wahyuningrum ◽  
Gita Fadila Fitriana
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Female Voice ◽  
Dominant Weight ◽  
Accuracy Rate ◽  
Male And Female ◽  
The Right ◽  
Mel Frequency Cepstral Coefficient ◽  
Selection Of

This study investigates the sound of Hanacaraka in Javanese to select the best frame feature in checking the reading sound. Selection of the right frame feature is needed in speech recognition because certain frames have accuracy at their dominant weight, so it is necessary to match frames with the best accuracy. Common and widely used feature extraction models include the Mel Frequency Cepstral Coefficient (MFCC). The MFCC method has an accuracy of 50% to 60%. This research uses MFCC and the selection of Dominant Weight features for the Javanese language script sound Hanacaraka which produces a frame and cepstral coefficient as feature extraction. The use of the cepstral coefficient ranges from 0 to 23 or as many as 24 cepstral coefficients. In comparison, the captured frame consists of 0 to 10 frames or consists of eleven frames. A sound sampling of 300 recorded voice sampling was tested on 300 voice recordings of both male and female voice recordings. The frequency used is 44,100 kHz 16-bit stereo. The accuracy results show that the MFCC method with the ninth frame selection has a higher accuracy rate of 86% than other frames.

scholarly journals Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection

Telematika ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 88
Author(s):  
Heriyanto Heriyanto
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dominant Weight ◽  
Accuracy Rate ◽  
Frame Selection ◽  
Good Night ◽  
Good Morning ◽  
The Right ◽  
Mel Frequency Cepstral Coefficient ◽  
Selection Of

Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.Originality/value/state of the art:Selection of the appropriate features on the frame.

scholarly journals Comparison of Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction, With and Without Framing Feature Selection, to Test the Shahada Recitation

2021 ◽  
Vol 1 (1) ◽  
pp. 335-354
Author(s):  
Heriyanto Heriyanto ◽  
Dyah Ayu Irawati
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dominant Weight ◽  
Accuracy Rate ◽  
Matching Accuracy ◽  
Cepstral Coefficients ◽  
The Right ◽  
Mel Frequency Cepstral Coefficient ◽  
The Voice ◽  
Selection Of

Voice research for feature extraction using MFCC. Introduction with feature extraction as the first step to get features. Features need to be done further through feature selection. The feature selection in this research used the Dominant Weight feature for the Shahada voice, which produced frames and cepstral coefficients as the feature extraction. The cepstral coefficient was used from 0 to 23 or 24 cepstral coefficients. At the same time, the taken frame consisted of 0 to 10 frames or eleven frames. Voting as many as 300 samples of recorded voices were tested on 200 voices of both male and female voice recordings. The frequency used was 44.100 kHz 16-bit stereo. This research aimed to gain accuracy by selecting the right features on the frame using MFCC feature extraction and matching accuracy with frame feature selection using the Dominant Weight Normalization (NBD). The accuracy results obtained that the MFCC method with the selection of the 9th frame had a higher accuracy rate of 86% compared to other frames. The MFCC without feature selection had an average of 60%. The conclusion was that selecting the right features in the 9th frame impacted the accuracy of the voice of shahada recitation.

scholarly journals The Implementation Of Mfcc Feature Extraction And Selection of Cepstral Coefficient for Qur’an Recitation in TPA (Qur’an Learning Center) Nurul Huda Plus Purbayan

2021 ◽  
Vol 1 (1) ◽  
pp. 453-478
Author(s):  
Heriyanto Heriyanto ◽  
Herlina Jayadianti ◽  
Juwairiah Juwairiah
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Automatic Speech Recognition System ◽  
Linear Predictive Code ◽  
Predictive Code ◽  
Feature Extraction And Selection ◽  
Approach Method ◽  
The Right ◽  
Mel Frequency Cepstral Coefficient ◽  
Selection Of

There are two approaches to Qur’an recitation, namely talaqqi and qira'ati. Both approaches use the science of recitation containing knowledge of the rules and procedures for reading the Qur'an properly. Talaqqi requires the teacher and students to sit facing each other while qira'ati is the recitation of the Qur'an with rhythms and tones. Many studies have developed an automatic speech recognition system for Qur’an recitation to help the learning process. Feature extraction model using Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive Code (LPC). The MFCC method has an accuracy of 50% to 60% while the accuracy of Linear Predictive Code (LPC) is only 45% to 50%, so the non-linear MFCC method has higher accuracy than the linear approach method. The cepstral coefficient feature that is used starts from 0 to 23 or 24 cepstral coefficients. Meanwhile, the frame taken consists of 0 to 10 frames or eleven frames. Voting for 300 recorded voice samples was tested against 200 voice recordings, both male and female voices. The frequency used was 44.100 kHz stereo 16 bit. This study aims to obtain good accuracy by selecting the right feature on the cepstral coefficient using MFCC feature extraction and matching accuracy through the selection of the cepstral coefficient feature with Dominant Weight Normalization (NBD) at TPA Nurul Huda Plus Purbayan. Accuracy results showed that the MFCC method with the selection of the 23rd cepstral coefficient has a higher accuracy rate of 90.2% compared to the others. It can be concluded that the selection of the right features on the 23rd cepstral coefficient affects the accuracy of the voice of Qur’an recitation.

scholarly journals Javanese Gender Speech Recognition Based on Machine Learning Using Random Forest and Neural Network

SISFORMA ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 50
Author(s):  
Kristiawan Nugroho
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Speech Recognition ◽  
Random Forest ◽  
Accuracy Rate ◽  
Word Error Rate ◽  
Human Voice ◽  
Preprocessing Technique ◽  
The Right

Speech is a means of communication between people throughout the world. At present research in the field of speech recognition continues to develop in producing a robust method in various research variants. However decreasing the word error rate or reducing noise is still a problem that is still being investigated until now. The purpose of this study is to find the right method with high accuracy to classify the gender voices of Javanese. This research used a human voice dataset of both men and women from the Javanese tribe which was recorded and then processed using a noise reduction preprocessing technique with the MFCC extraction feature method and then classified using 2 machine learning methods, namely Random Forest and Neural Network. Evaluation results indicate that the classification of Javanese accent speech accents results in an accuracy rate of 91.3 % using Random Forest and 92.2% using Neural Network.

WAVELET FEATURES BASED SLEEP STAGES DETECTION USING SINGLE CHANNEL EEG

2017 ◽  
Vol 5 (4) ◽  
pp. 99-102
Author(s):  
Vandana Roy ◽  
Anand Prakash ◽  
Shailja Shukla
Keyword(s):  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Single Channel ◽  
Kappa Coefficient ◽  
Sleep Stages ◽  
Chi Square ◽  
Wavelet Features ◽  
Stage Classification ◽  
Selection Of

The sleep stages determination is important for the identification and diagnosis of different diseases. An efficient algorithm of wavelet decomposition is used for feature extraction of single channel EEG. The Chi-Square method is applied for the selection of the best attributes from the extracted features. The classification of different staged techniques is applied with the help AdaBoost.M1 algorithm. The accuracy of 89.82% achieved in the six stage classification. The weighted sensitivity of all stages is 89.8% and kappa coefficient of 77.93% is obtained in the six stage classification.

scholarly journals PENERAPAN METODE CLUSTERING DENGAN ALGORITMA K-MEANS PADA PENGELOMPOKAN DATA PENGHASILAN ORANG TUA SISWA

2021 ◽  
Vol 5 (1) ◽  
pp. 125-133
Author(s):  
Dionisius Missa ◽  
Sentot Achmadi ◽  
Ali Mahmudi
Keyword(s):  
Drop Out ◽  
Economic Factors ◽  
Tuition Fees ◽  
Accuracy Rate ◽  
Student Data ◽  
Number Of Children ◽  
Cost Of Education ◽  
The Social ◽  
The Right

In Indonesia there are still many students who cannot continue their education because of the high cost of education. One of the reasons for students not continuing their education is the social and economic factors that affect their parents. Therefore, the school does not want students to drop out of school because the parents are unable to pay the full tuition fee each semester of the increase to the right students and are not wrongly targeted so that students can complete their education without being burdened. unpaid tuition fees. In determining usually there are problems because of the large number of student data which causes problems in grouping student data, it is likely that errors will occur when determining students who really have to delay payment. For this research, a system was built in order to be able to apply the K-Means method, in assisting schools in determining the classification of student data that requires a delay in payment every time the semester increases with the right target. So that this process can help schools in shortening the process of processing a lot of student data. While the criteria used in the system are 5, namely father's job, father's income, mother's job, mother's income and number of children. The system is designed using CodeIgniter, PHP, JavaScript and MYSQL as the database. In the process of application testing results that have used the K-Means method for the process of grouping student data that requires late payment, it can produce an accuracy rate of 85%. For this testing process, it is carried out by comparing old student data with the results of the K-Means calculation, so that this system is expected to help the school in classifying student data accurately.

Disease Classification Using ECG Signals Based on R-Peak Analysis With ABC and ANN

2019 ◽  
Vol 8 (2) ◽  
pp. 67-86 ◽  
Author(s):  
Suman Lata ◽  
Rakesh Kumar
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Classification Accuracy ◽  
Disease Classification ◽  
Cardiac Diseases ◽  
Ecg Signals ◽  
Processing Steps ◽  
Ann Classification ◽  
Selection Of

ECG feature extraction has an important role in identifying a number of cardiac diseases. Lots of work has been done in this field but the most important challenges faced in previous work are the selection of proper R-peaks and R-R intervals due to the lack of appropriate pre-processing steps like decomposition, smoothing, filtering, etc., and the optimization of the features for proper classification. In this article, DWT-based pre-processing and ABC is used for optimization of features which helps to achieve better classification accuracy. It is utilized for initial diagnosis of abnormalities. The signals are taken from MIT-BIH arrhythmia database for the analysis. The aim of the research is to classification of six diseases; Normal, Atrial, Paced, PVC, LBBB, RBBB with an ABC optimization algorithm and an ANN classification algorithm on the basis of the extracted features. Various parameters, like, FAR, FRR, and accuracy are measured for the execution. Comparative analysis is shown of the proposed and the existing work to depict the effectiveness of the work.

Analisis Kapasitas Galangan Kapal Ikan Untuk Memenuhi Rencana Pengadaan Kapal Ikan Hibah

2019 ◽  
Vol 13 (1) ◽  
pp. 25-36
Author(s):  
Agus Lubis Fitriansyah ◽  
Heri Supomo
Keyword(s):  
Fishing Vessel ◽  
Fishing Vessels ◽  
Score Method ◽  
The Government ◽  
The Right ◽  
Type 3 ◽  
Selection Of

The government through the Ministry of Marine and Fisheries offers assistance of fishing vessel to achieve fisheries production targets. This procurement plan must be supported by the ability and selection of the right shipyard. Beacuse the information of the capability and capacity of fiber shipyards in Indonesia is unclear, so the realization of the procurement of fishing vessel in previous years did not met the planned targets. The purpose of this study was to analyze shipyard capacity to meet the planned procurement of KKP fishing vessels grant in 2019. First classification of fishing vessels is based on the size of each GT, which is 5 GT (type 1), 5-10 GT (type 2), and 20-30 GT (type 3). The second is the minimum shipyard criteria for building fishing boats. Third, an assessment of the shipyard is based on the criteria that have been made. Fourth, shipyard selection was carried out on each WPPN-RI using the load score method. The fifth calculates the number of ships that can be built by the shipyard. The results of the shipyard assessment found that 43% of shipyards have the ability to build type 1 vessels, around 38% of shipyards have the ability to build type 2 vessels, and around 19% of shipyards have the ability to build type 3 vessels. is 1625 units / period. Referring to shipyard capacity, it can be said that the entire shipyard is able to fulfill the plan to procure assistance for KKP fishing vessels in the 2019 budget year.

Test Of Vowels In Speech Recognition Using Continuous Density Hidden Markov Model And Development Of Phonetically Balanced-Words In The Filipino Language

2014 ◽  
Vol 1 (1) ◽  
pp. 531-536
Author(s):  
Arnel C. Fajardo ◽  
Yoon-joong Kim
Keyword(s):  
Speech Recognition ◽  
Language Processing ◽  
Word List ◽  
Accuracy Rate ◽  
Continuous Density ◽  
Average Accuracy ◽  
Speaker Independent ◽  
Independent Test ◽  
Final Output ◽  
Mel Frequency Cepstral Coefficient

AbstractAn Automatic Speech Recognition (ASR) converts the speech signals into words. The recognized words can be the final output or it can be an input for a natural language processing. In this paper, vowel recognizer using Continuous density HMM and Mel-Frequency Cepstral Coefficient (MFCC) were used for feature extraction for its development, and phonetically balanced words (PBW) in Filipino were developed. Thus, this study is a preparation for Filipino Language ASR using HMM. For vowel recognizer, forty speakers were trained (20 male and 20 female speakers). An average accuracy rate of 94.5% was achieved for speaker-dependent test and 90.8% for speaker independent test. For PBW, 2 word lists were developed consisting of 257 words for the 2-syllable Filipino PBW word list and 212 words for the 3-syllable Filipino PBW word list.

Research of Diver Sonar Image Recognition Based on Support Vector Machine

2013 ◽  
Vol 785-786 ◽  
pp. 1437-1440 ◽  
Author(s):  
Ke Li ◽  
Chong Lun Li ◽  
Wei Zhang
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Small Object ◽  
Accuracy Rate ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Feature Parameters ◽  
Sonar Images ◽  
The Right ◽  
Selection Of

To recognize small diver target from the dim special diver sonar images accurately, the Support Vector Machine method is used as classifier. According to the main characteristics of diver, five feature parameters, including Average-scale, Velocity, Shape, Direction, Included angle, are chosen as the input of characteristics vectors to train the net. And then the testing images are classified and identified. The experimental results show that accuracy rate of recognition reaches 94.5% for as many as 200 testing images. The experiment indicates that small object recognition from complex sonar images based on the right selection of feature parameters is of good performance by using the SVM method as well as good engineering foreground.

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