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

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.

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

JURNAL INFOTEL ◽

10.20895/infotel.v13i2.657 ◽

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.

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

Telematika ◽

10.31315/telematika.v18i1.4495 ◽

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.

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

RSF Conference Series: Engineering and Technology ◽

10.31098/cset.v1i1.395 ◽

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.

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A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

International Journal of Emerging Research in Management and Technology ◽

10.23956/ijermt.v6i6.287 ◽

2018 ◽

Vol 6 (6) ◽

pp. 307

Author(s):

Manish M. Kayasth ◽

Bharat C. Patel

Keyword(s):

Feature Extraction ◽

Character Recognition ◽

Recognition Rate ◽

Recognition System ◽

Post Processing ◽

Classification Technique ◽

Scanned Image ◽

Gujarati Language ◽

High Degree ◽

Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition.

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A new hybrid approach for feature extraction and selection of electroencephalogram signals in case of person recognition

Journal of Reliable Intelligent Environments ◽

10.1007/s40860-021-00148-z ◽

2021 ◽

Author(s):

Bhawna Kaliraman ◽

Manoj Duhan

Keyword(s):

Feature Extraction ◽

Hybrid Approach ◽

Person Recognition ◽

Feature Extraction And Selection ◽

Selection Of

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Penerapan K-Means Clustering Untuk Seleksi Frame Dominan Berbasis NTSC Pada Obyek Bergerak

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020742184 ◽

2020 ◽

Vol 7 (4) ◽

pp. 745

Author(s):

Rizka Indah Armianti ◽

Achmad Fanany Onnilita Gaffar ◽

Arief Bramanto Wicaksono Putra

Keyword(s):

Feature Extraction ◽

Video Data ◽

Clustering Method ◽

Data Frame ◽

First Order ◽

Feature Extraction And Selection ◽

Different Shapes ◽

Frame Set ◽

Selection Of

Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap frame. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap frame-nya. Frame yang memiliki pola terbaik diantara frame lainnya disebut frame dominan. Penelitian ini bertujuan untuk menyeleksi frame dominan dari rangkaian frame dengan menerapkan metode K-means clustering untuk memperoleh centroid dominan (centroid dengan nilai tertinggi) yang digunakan sebagai dasar seleksi frame dominan. Dalam menyeleksi frame dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu Mean. Data hasil ekstraksi ciri berjumlah 93 data frame yang selanjutnya dikelompokkan menjadi 3 cluster menggunakan metode K-Means. Dari hasil clustering, centroid dominan terletak pada cluster 3 dengan nilai centroid 0.0177 dan terdiri dari 41 data frame. Selanjutnya diukur jarak kedekatan seluruh data cluster 3 terhadap centroid, data yang memiliki jarak terdekat dengan centroid itulah frame dominan. Hasil seleksi frame dominan ditunjukkan pada jarak antar centroid dengan anggota cluster, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai 0.0010 yang dimiliki oleh frame ke-59, ke-36 dan ke-35. AbstractThe object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.

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Simultaneous feature extraction and selection of microarray data using fuzzy-rough based multiobjective nonnegative matrix factorization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-17954 ◽

2017 ◽

Vol 33 (6) ◽

pp. 4043-4053

Author(s):

Mohamed E. Abd Elaziz

Keyword(s):

Feature Extraction ◽

Microarray Data ◽

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Nonnegative Matrix ◽

Feature Extraction And Selection ◽

Selection Of

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Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv034 ◽

2015 ◽

Vol 22 (5) ◽

pp. 993-1000 ◽

Cited By ~ 68

Author(s):

Sheng Yu ◽

Katherine P Liao ◽

Stanley Y Shaw ◽

Vivian S Gainer ◽

Susanne E Churchill ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Feature Extraction ◽

High Throughput ◽

Knowledge Sources ◽

Phenotype Classification ◽

Feature Extraction And Selection ◽

Automated Feature Extraction ◽

High Throughput Phenotyping ◽

Text Features ◽

Selection Of

Abstract Objective Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Materials and methods Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. Results The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Discussion Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. Conclusion The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.

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Speakar-Independent Isolated Word Recognition using HTK for Varhadi – a Dialect of Marathi

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3832.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 748-751

Keyword(s):

Speech Recognition ◽

Viterbi Algorithm ◽

Native Speakers ◽

Recognition Performance ◽

Recognition System ◽

Automatic Speech Recognition System ◽

Test Dataset ◽

Regional Language ◽

Unknown Words ◽

Mel Frequency Cepstral Coefficient

Speech recognition is widely used in the computer science to make well-organized communication between humans and computers. This paper addresses the problem of speech recognition for Varhadi, the regional language of the state of Maharashtra in India. Varhadi is widely spoken in Maharashtra state especially in Vidharbh region. Viterbi algorithm is used to recognize unknown words using Hidden Markov Model (HMM). The dataset is developed to train the system consists of 83 isolated Varhadi words. A Mel frequency cepstral coefficient (MFCCs) is used as feature extraction to perform the acoustical analysis of speech signal. Word model is implemented in speaker independent mode for the proposed varhadi automatic speech recognition system (V-ASR). The training and test dataset consist of isolated words uttered by 8 native speakers of Varhadi language. The V-ASR system has recognized the Varhadi words satisfactorily with 92.77%. recognition performance.

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A Combined Method for On-Line Signature Verification

Cybernetics and Information Technologies ◽

10.2478/cait-2014-0022 ◽

2014 ◽

Vol 14 (2) ◽

pp. 92-97

Author(s):

Desislava Boyadzhieva ◽

Georgi Gluhchev

Keyword(s):

Feature Extraction ◽

Recognition System ◽

Signature Verification ◽

System Evaluation ◽

Combined Method ◽

Average Accuracy ◽

Feature Extraction And Selection ◽

On Line ◽

Signature Recognition

Abstract A combined method for on-line signature verification is presented in this paper. Moreover, all the necessary steps in developing a signature recognition system are described: signature data pre-processing, feature extraction and selection, verification and system evaluation. NNs are used for verification. The influence of the signature forgery type (random and skilled) over the verification results is investigated as well. The experiments are carried out on SUsig database which consists of genuine and forgery signatures of 89 users. The average accuracy is 98.46%.

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