Sistem Kontrol Robot Arm 5 DOF Berbasis Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (MFCC) dan Adaptive Neuro-Fuzzy Inference System (ANFIS)

Sanjaya, W. S. Mada; Anggraeni, Dyah

doi:10.17509/wafi.v1i2.4277

Cited by 15 publications

(13 citation statements)

References 6 publications

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“…Matching research on speech recognition using different methods produces different outputs, including through neural networks [18], Hidden Markov Model (HMM) [4], speech recognition with Dynamic Time Wrapping (DTW) [ 20]. Speech recognition research using the feature extraction method MFCC has been widely carried out in all fields, including applied language.…”

Section: B Matching Speech Recognitionmentioning

confidence: 99%

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

Heriyanto

Wahyuningrum²,

Fitriana³

2021

J.INFOTEL

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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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Section: B Matching Speech Recognitionmentioning

confidence: 99%

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

Heriyanto

Wahyuningrum²,

Fitriana³

2021

J.INFOTEL

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“…Hasil dari preprocessing ini akan diekstraksi dengan MFCC untuk menghasilkan MFCC feature (Panchal, 2010). MFCC adalah metode yang paling banyak digunakan dalam pemrosesan suara, karena dianggap cukup baik dalam merepresentasikan sinyal suara (Sanjaya & Salleh, 2014 Dari nilai tersebut maka jumlah frame yang akan dihasilnya sebanyak 60 dimana setiap frame terdapat 512 sinyal suara. Gambar 6 berikut menunjukkan hasil grafik sinyal sesudah dipecah menjadi beberapa bagian.…”

Section: Feature Extraction Dengan Mfccunclassified

Penerapan Hidden Markov Model (HMM) dan Mel-Frequency Cesptral Coefficients (MFCC) pada E-Learning Bahasa Madura untuk Anak Usia Dini

Ubaidi

Dewi

2020

JTIIK

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Bahasa Madura adalah Bahasa Daerah yang digunakan di Pulau Madura. Bahasa ini memiliki banyak variasi pengucapan dan dialek. Hal ini menyebabkan Bahasa Madura tidak mudah untuk dipelajari bahkan oleh masyarakat Madura khususnya anak-anak. Saat ini belum ada media pembelajaran Bahasa Madura yang menarik untuk mempelajari Bahasa Madura. Padahal melalui pembelajaran yang menyenangkan diharapkan dapat membantu anak untuk memperoleh kemampuan melatih penerapan pengucapan nama binatang, angka, buah dan benda dalam Bahasa Madura. Karena itulah perlu dibuat e-learning Bahasa Madura dengan menerapkan pengenalan pola suara sehingga dapat membantu anak mengenal Bahasa Madura yang memiliki variasi pengucapan untuk objek yang sama. Aplikasi e-learning Bahasa Madura untuk anak usia dini digunakan untuk mengenalkan nama objek dalam Bahasa Madura melalui pengenalan pola suara yang diucapkan dibuat dengan menggunakan teknik Mel-Frequency Cesptral Coefficients (MFCC) sebagai ekstrak fitur suara dan Hidden Markov Model (HMM) sebagai teknik pembelajarannya. Penerapan MFCC dan HMM untuk pengenalan pengucapan Bahasa Daerah khususnya Bahasa Madura belum pernah ada sebelumnya, sehingga dengan adanya penelitian ini diharapkan dapat membantu anak usia dini mengenal pengucapan kata Bahasa Madura dengan benar. Pada penelitian ini, sejumlah anak direkam suaranya untuk dijadikan sebagai data training. Data suara yang digunakan adalah data suara yang pengucapan dianggap benar. Skenario percobaan dilakukan dengan menggunakan satu model dan multi model. Setelah dilakukan serangkaian percobaan, hasil penelitian menunjukkan yaitu rata-rata akurasi untuk pengujian sistem dengan satu model yaitu 73% dengan akurasi tertinggi 75% dan rata-rata akurasi untuk pengujian sistem dengan multi model yaitu 80% dengan akurasi tertinggi 81%. AbstractBahasa Madura is a regional language used in Madura island. This language has many variations of pronunciation and dialect that makes it not easy to learn, even by the local people especially children. There hasn’t been any interesting learning media to learn Bahasa Madura so far. In fact, a fun learning activity is needed to help children to enhance their ability in pronouncing animals’ names, numbers, fruits and things in Bahasa Madura. Thus, it’s considered important to create Bahasa Madura e-learning by implementing the recognition of voice patterns in order to make it easier for the children to learn Bahasa Madura which has several variations of pronunciation only for one single object. This Bahasa Madura e-learning application for young learners is used to introduce Bahasa Madura vocabularies by recognizing the voice pattern recordings which have been processed through MFCC technique as the extracted voice features and HMM as the learning techniques. The implementation of MFCC and HMM as the learning tool to introduce the pronunciation of regional language vocabularies especially Bahasa Madura has never been done before. Therefore, this research is expected to help the young learners to be able to pronounce Bahasa Madura vocabularies properly. In this study, a number of young learners’ voices were recorded and were set as the trial data. Only the proper voice data that were used—voice data that were considered to be pronounced correctly. The trial method was done through one-single model and multi-model. After doing several simultaneous trials, the result showed the accuracy level. The average accuracy level for one-single model system was 73% (with the highest accuracy reached 75%) and the average accuracy level for multi-model system was 80% (with the highest accuracy reached 81%).

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“…Langkah-langkah proses MFCC berdasarkan pada perbedaan dari frekuensi yang terdengar oleh pendengaran manusia melalui panca indranya, sehingga dapat seperti layaknya manusia merepresentasikan sinyal suara. Proses ekstraksi ciri MFCC [14] adalah sebagai berikut:…”

Section: Metode Ektraksi Ciri Mfccunclassified

Analisis Bentuk Pola Suara Menggunakan Ekstraksi Ciri Mel-Frequencey Cepstral Coefficients (MFCC)

Umar¹,

Riadi²,

Hanif

2019

CogITo Smart Journal

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Sound is a part of the human body that is unique and can be distinguished, so its application can be used in sound pattern recognition technology, one of which is used for sound biometrics. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study discusses the analysis of the form of a sound pattern that aims to determine the shape of the sound pattern of a person's character based on the spoken voice input. This study uses the Melf-Frequency Cepstrum Coefficients (MFCC) method for feature extraction process from speaker speech signals. The MFCC process will convert the sound signal into several feature vectors which will then be displayed in graphical form. Analysis and design of sound patterns using Matlab 2017a software. Tests were carried out on 5 users consisting of 3 men and 2 women, each user said 1 predetermined "LOGIN" word, which for 15 words said. The results of the test are the form of a sound pattern between the characteristics of 1 user with other users. Keywords—Voice, Pattern, Feature Extraction, MFCC

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Sistem Kontrol Robot Arm 5 DOF Berbasis Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (MFCC) dan Adaptive Neuro-Fuzzy Inference System (ANFIS)

Cited by 15 publications

References 6 publications

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

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

Penerapan Hidden Markov Model (HMM) dan Mel-Frequency Cesptral Coefficients (MFCC) pada E-Learning Bahasa Madura untuk Anak Usia Dini

Analisis Bentuk Pola Suara Menggunakan Ekstraksi Ciri Mel-Frequencey Cepstral Coefficients (MFCC)

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