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 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 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 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.

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.

Feature Extraction and Selection in Archaeological Images for Automatic Annotation

2021 ◽  
pp. 2250006
Author(s):  
Marwa Ben Salah ◽  
Ameni Yengui ◽  
Mahmoud Neji
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Image Feature ◽  
Contour Method ◽  
Automatic Annotation ◽  
Extraction Techniques ◽  
Feature Extraction And Selection ◽  
Selection Stage ◽  
Feature Selection Techniques ◽  
Selection Of

In this paper, we present two steps in the process of automatic annotation in archeological images. These steps are feature extraction and feature selection. We focus our research on archeological images which are very much studied in our days. It presents the most important steps in the process of automatic annotation in an image. Feature extraction techniques are applied to get the feature that will be used in classifying and recognizing the images. Also, the selection of characteristics reduces the number of unattractive characteristics. However, we reviewed various images of feature extraction techniques to analyze the archaeological images. Each feature represents one or more feature descriptors in the archeological images. We focus on the descriptor shape of the archaeological objects extraction in the images using contour method-based shape recognition of the monuments. So, the feature selection stage serves to acquire the most interesting characteristics to improve the accuracy of the classification. In the feature selection section, we present a comparative study between feature selection techniques. Then we give our proposal of application of methods of selection of the characteristics of the archaeological images. Finally, we calculate the performance of two steps already mentioned: the extraction of characteristics and the selection of characteristics.

scholarly journals DETEKSI UCAPAN ANGKA SATU SAMPAI SEPULUH BAHASA PALEMBANG MENGGUNAKAN MFCC DAN BOBOT DOMINAN

Telematika ◽  
2019 ◽  
Vol 16 (1) ◽  
pp. 52
Author(s):  
Heriyanto Heriyanto Heriyanto
Keyword(s):  
Feature Selection ◽  
Test Results ◽  
Second Stage ◽  
Regional Language ◽  
Proposed Model ◽  
Third Stage ◽  
Dominant Weights ◽  
Three Stages ◽  
Mel Frequency Cepstral Coefficient ◽  
Selection Of

Abstract Detecting speech with regional language, one of which is Palembang language, has uniqueness and distinctiveness in accent. Differences in dialects to check how precise and influential the accuracy of using MFCC and dominant weights. This study consists of three stages. The first stage, feature extraction of numerical numbers from one to ten using Mel Frequency Cepstral Coefficient (MFCC). The second stage is the selection of features that will be used as feature tables using the proposed model Normalized Dominant Weight (NBD) with threshold similarity, range, filtering, normalization of weights and dominant weights. The third stage is testing by checking by finding similarities in range, filtering, sequential multiplication and calculation of Suitability of Uniformity Patterns (CTF). The test results of checking MFCC and feature selection with normalization of dominant weights were 70% while without feature selection only 42%. Keywords : extraction, weighting, dominant, normalization, range Abstrak Deteksi ucapan dengan berbahasa daerah salah satunya bahasa Palembang mempunyai keunikan dan kekhasan dalam logat berbahasa. Perbedaan logat bahasa untuk mengecekan seberapa tepat dan berpengaruh terhadap akurasi menggunakan MFCC dan Bobot dominan. Penelitian ini terdiri atas tiga tahap. Tahap pertama, ekstraksi ciri angka bahasa angka satu sampai sepuluh menggunakan Mel Frequency Cepstral Coefficient (MFCC). Tahap kedua adalah pemilihan fitur yang akan dijadikan tabel fitur menggunakan model yang diusulkan Normalisasi Bobot Dominan (NBD) dengan kesamaan threshold, jangkauan, filtering, normalisasi bobot dan bobot dominan. Tahap ketiga adalah pengujian dengan pengecekan dengan cara mencari kesamaan jangkauan, filtering, perkalian sekuensial dan perhitungan Kesesuaian Keseragaman Pola (KKP). Hasil pengujian pengecekan terhadap MFCC dan pemilihan fitur dengan normalisasi bobot dominan sebesar 70% sedangkan tanpa pemilihan fitur hanya sebesar 42%. Kata kunci : ekstraksi, bobot, dominan, normalisasi, jangkauan

Damage detection of bridges based on combining efficient cepstral coefficients

2020 ◽  
pp. 107754632095834
Author(s):  
Hossein Babajanian Bisheh ◽  
Gholamreza Ghodrati Amiri ◽  
Masoud Nekooei ◽  
Ehsan Darvishan
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Damage Detection ◽  
Structural Damage ◽  
Damage Index ◽  
Support Vector ◽  
Selection Methods ◽  
Significant Information ◽  
Effective Coefficients ◽  
Cepstral Coefficients

In this article, a novel vibration-based damage detection approach is proposed based on selecting effective cepstral coefficients, consisting of three main stages: (1) signal processing and feature extraction, (2) damage detection by combining effective cepstral coefficients through feature selection methods, and (3) performance evaluation. First, two feature extraction techniques are used in damage identification systems, including linear prediction cepstral coefficients and mel frequency cepstral coefficients. Second, to improve the performance of damage detection, the combination of the effective cepstral coefficients is proposed as a damage index. By applying several feature selection methods, the most effective coefficients are found and then combined to create a subset that carries the most significant information about the structural damage. Finally, the support vector machine classifier is performed to evaluate the proposed approach in detecting the structural damage. The proposed technique is verified using a suite of numerical and full-scale studies. Results confirm that the proposed method achieves a significant performance with great accuracy and reduces false alarms.

scholarly journals The Multi Attribute Utility Theory (Death) Method In The Decision Of The Distributor Distributor Selection (Metode Multi Attribute Utility Theory (Maut) Dalam Keputusan Pemilihan Distributor Barang)

KOMTEKINFO ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 84-100
Author(s):  
Ritna Wahyuni ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Decision Support ◽  
Utility Theory ◽  
Convenience Store ◽  
Test Results ◽  
Accuracy Rate ◽  
Sales Process ◽  
Multi Attribute Utility Theory ◽  
The Right ◽  
Selection Of

Distributors are intermediaries who distribute products from factories to retailers. While the distributor of goods is the distributor of goods from factories to shops that need these goods. Incorrect selection of distributors can interfere with the sales process at the store. To improve the quality and quality of a store, it requires the best distributor of goods. This study aims to determine the best distributor of goods. The method used is the Multi Attribute Utility Theory (MAUT) of distributor data at the Padang Luar Sundanese Convenience Store. The data processed in this study consisted of a number of distributor data selected by the Multipurpose Store. From some of the distributor data, the Decision Support System is very necessary in the selection of distributors who aim for the selection of appropriate alternative decisions. The selection of distributors uses 15 samples of distributor data and 5 criteria data that are used as the basis for selecting distributors, namely quality of goods, affordable prices, strategic locations, service responses, and giving bonuses. The results of testing on this method obtained an accuracy rate of 86.67% of the right distributors and in accordance with the realization of the UI data. So this research is very suitable in choosing the best distributor. From the test results, it has got the 5 best distributors by assigning a weight of 11.50 to the best distributor, so the criteria set by the All-Round Shop can be used as a reference in the selection of distributors of goods.

Feature Extraction from Spectral Data Using the Bayesian Evidence Framework

2009 ◽  
Vol 413-414 ◽  
pp. 151-158
Author(s):  
James J. Hensman ◽  
Rob J. Barthorpe
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Damage Identification ◽  
Large Datasets ◽  
Optimal Selection ◽  
Evidence Framework ◽  
Bayesian Evidence ◽  
Experimental Problem ◽  
Bayesian Evidence Framework ◽  
Selection Of

The optimal selection of discriminatory features from large datasets remains a pressing problem in damage identification. In this paper, a Bayesian approach to classification and feature selection is introduced and applied to a challenging experimental problem.

Selection of Wavelet Features for Biomedical Signals Using SVM Learning

2016 ◽  
pp. 299-308
Author(s):  
Girisha Garg ◽  
Vijander Singh
Keyword(s):  
Support Vector Machine ◽  
Signal Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Feature Space ◽  
Support Vector ◽  
Feature Extraction And Selection ◽  
Wavelet Features ◽  
Wavelet Decompositions ◽  
Selection Of

Signal processing problems require feature extraction and selection techniques. A novel Wavelet Feature Selection algorithm is proposed for ranking and selecting the features from the wavelet decompositions. The algorithm makes use of support vector machine to rank the features and backward feature elimination to remove the features. The finally selected features are used as patterns for the classification system. Two EEG datasets are used to test the algorithm. The results confirm that the algorithm is able to improve the efficiency of wavelet features in terms of accuracy and feature space.

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