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Alif Fajri Ryamizard - One of the best experts on this subject based on the ideXlab platform.
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deteksi nada tunggal alat musik kecapi bugis makassar menggunakan metode mel frequency Cepstral Coefficient mfcc dan klasifikasi k nearest neighbour knn
eProceedings of Engineering, 2018Co-Authors: Alif Fajri Ryamizard, Bambang Hidayat, Sofia SaidahAbstract:Abstrak Alat musik tradisional merupakan salah satu komoditas Indonesia yang menjadi asset yang berharga dan telah menjadi salah satu daya tarik Indonesia bagi warga asing. Hampir setiap daerah di Indonesia memiliki alat musik tradisional masing-masing. Nada yang unik menjadi ciri khas berbagai alat musik tradisional Indonesia, seperti alat musik kecapi yang berasal dari Sulawesi Selatan. Kecapi sering digunakan dalam festival musik Sulawesi di berbagai daerah, namun sering terdapat permasalahan pada saat penyetelan alat musik kecapi karena membutuhkan waktu cukup lama. Pada Tugas Akhir ini telah dirancang sebuah sistem yang dapat mengidentifikasi nada yang terdapat pada alat musik kecapi melalui pengolahan suara. Pada sistem identifikasi nada alat musik ini terdiri dari ekstraksi ciri dan pengklasifikasi nada alat musik kecapi. Melalui ekstraksi ciri dari suatu sinyal audio dapat diketahui jenis nada dan karakteristiknya. Metode ekstraksi ciri yang digunakan adalah Mel Frequency Cepstral Coefficient dan metode klasifikasi yang digunakan yaitu K-Nearest Neighbor. Tugas Akhir ini diharapkan dapat membangun suatu sistem yang dapat mengenali nada pada alat musik kecapi. Nada yang dideteksi terdiri dari 7 nada, yaitu do, re, mi, fa, sol, la, si. Tingkat akurasi yang telah diharapkan sebesar 70%, dimana nada masukan berasal dari microphone. Kata Kunci: Kecapi, Mel Frequency Cepstral Coefficient, K-Nearest Neighbor Abstract Traditional musical instrument is one of Indonesia's commodities which become a valuable asset and has become one of Indonesia's attraction for foreigners. Almost every region in Indonesia has their own traditional musical instruments. Unique tones characterize a variety of traditional Indonesian musical instruments, such as kecapi instruments originating from South Sulawesi. Kecapi is often used in Sulawesi music festivals in various regions, but there are often problems at the time of adjusting the lute because it takes a long time. In this Final Project has been designed a system that can identify the tone contained in the instruments of the lute through sound processing. The tone identification system of this instrument consists of characteristic extraction and the classification of the tone of the lute music instrument. Through the characteristic extraction of an audio signal can be known the type of tone and its characteristics. The method of feature extraction used is Mel Frequency Cepstral Coefficient and the method of classification used is K-Nearest Neighbor. This Final Project is expected to build a system that can recognize the tone of the lute instrument. Detected tone consists of 7 tones, namely do, re, mi, fa, sol, la, si. The expected accuracy rate is 70%, where the input tone comes from the microphone. Keywords: Kecapi, Mel Frequency Cepstral Coefficient, K-Nearest Neighbor
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DETEKSI NADA TUNGGAL ALAT MUSIK KECAPI BUGIS MAKASSAR MENGGUNAKAN METODE MEL FREQUENCY Cepstral Coefficient (MFCC) DAN KLASIFIKASI K-NEAREST NEIGHBOUR (KNN)
Universitas Telkom, 2018Co-Authors: Alif Fajri RyamizardAbstract:ABSTRAK Alat musik tradisional merupakan salah satu komoditas Indonesia yang menjadi asset yang berharga dan telah menjadi salah satu daya tarik Indonesia bagi warga asing. Hampir setiap daerah di Indonesia memiliki alat musik tradisional masing-masing. Nada yang unik menjadi ciri khas berbagai alat musik tradisional Indonesia, seperti alat musik kecapi yang berasal dari Sulawesi Selatan. Kecapi sering digunakan dalam festival musik Sulawesi di berbagai daerah, namun sering terdapat permasalahan pada saat penyetelan alat musik kecapi karena membutuhkan waktu cukup lama dan menggunakan pemahaman nada dalam menentukan nada yang sesuai dengan nada nada tertentu. Pada Tugas Akhir ini telah dirancang sebuah sistem yang dapat mengidentifikasi nada yang terdapat pada alat musik kecapi melalui pengolahan suara. Pada sistem identifikasi nada alat musik ini terdiri dari ekstraksi ciri dan pengklasifikasi nada alat musik kecapi. Melalui ekstraksi ciri dari suatu sinyal audio dapat diketahui jenis nada dan karakteristiknya. Metode ekstraksi ciri yang digunakan adalah Mel Frequency Cepstral Coefficient . MFCC adalah metode ekstraksi ciri yang mengadopsi sistem pendengaran manusia sebagai filter pengambilan informasi dari domain frekuensi sinyal. Sedangkan metode klasifikasi yang digunakan yaitu K-Nearest Neighbor. KNN adalah sebuah metode untuk melakukan klasifikasi terhadap objek berdasarkan data pembelajaran yang jaraknya paling dekat dengan objek tersebut. Tugas Akhir ini dapat membangun suatu sistem yang dapat mengenali nada pada alat musik kecapi. Nada yang dideteksi terdiri dari 7 nada, yaitu do, re, mi, fa, sol, la, si. Tingkat akurasi yang telah diharapkan sebesar 70%, dimana nada masukan berasal dari microphone. Kata Kunci : Kecapi, Mel Frequency Cepstral Coefficient, K-Nearest Neighbo
Heriyanto Heriyanto - One of the best experts on this subject based on the ideXlab platform.
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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
RSF Conference Series: Engineering and Technology, 2021Co-Authors: Heriyanto Heriyanto, Herlina Jayadianti, Juwairiah JuwairiahAbstract: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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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, 2021Co-Authors: Heriyanto Heriyanto, Dyah Ayu IrawatiAbstract: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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good morning to good night greeting classification using mel frequency Cepstral Coefficient mfcc feature extraction and frame feature selection
Telematika, 2021Co-Authors: Heriyanto HeriyantoAbstract:Purpose : Select the right features on the frame for good accuracy Design/methodology/approach: Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) Features Findings/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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Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features
'LPPM Institut Teknologi Telkom Purwokerto', 2021Co-Authors: Heriyanto Heriyanto, Wahyuningrum Tenia, Fitriana, Gita FadilaAbstract: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.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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Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features
'LPPM Institut Teknologi Telkom Purwokerto', 2021Co-Authors: Heriyanto Heriyanto, Tenia Wahyuningrum, Gita Fadila FitrianaAbstract: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
Afif M. Ridwansyah - One of the best experts on this subject based on the ideXlab platform.
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RANCANG BANGUN KUNCI BERBASIS SUARA PADA PINTU PINTAR DENGAN MENGGUNAKAN METODE MEL FREQUENCY Cepstral Coefficient (MFCC) DAN K-NEAREST NEIGHBOR (K-NN)
Universitas Telkom, 2018Co-Authors: Afif M. RidwansyahAbstract:Automatic Speech Recognition (ASR) adalah suatu sistem yang dapat mengenali, membandingkan dan mencocokkan pola suara masukan sistem tersebut dengan pola suara yang telah disimpan dalam memori. ASR terbagi menjadi dua jenis, yaitu Speech Recognition dan Speaker Recognition. Speaker Recognition adalah pengenalan identitas berdasarkan suara yang dikeluarkan (berupa intonasi suara, kedalaman suara, dan sebagainya). Pada penelitian ini dibangun sistem kunci berbasis suara dengan memanfaatkan Speaker Recognition. Pada penelitian ini digunakan metode Mel-Frequency Cepstral Coefficient (MFCC) sebagai ekstraksi ciri dan metode K-Nearest Neighbor (K-NN) sebagai klasifikasi ciri. Alat ini bekerja melalui dua tahapan, yaitu tahap pelatihan (training) dan tahap pengujian (testing). Hasil pengujian menunjukkan MFCC dan K-NN berhasil diimplementasikan dengan jumlah filterbank terbaik berjumlah 20 dan nilai koefisien terbaik sebanyak 13 koefisien dengan akurasi 100%. Hasil pengujian menunjukkan bahwa jumlah filterbank dan nilai koefisien mempengaruhi akurasi dari sistem. Kata kunci: Automatic Speech Recognition (ASR), biometrik suara, K-Nearest Neighbor (K-NN), Mel-Frequency Cepstral Coefficient (MFCC)
Sugondo Hadiyoso - One of the best experts on this subject based on the ideXlab platform.
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rancang bangun kunci berbasis suara pada pintu pintar dengan menggunakan metode mel frequency Cepstral Coefficient mfcc dan k nearest neighbor k nn
eProceedings of Engineering, 2018Co-Authors: Muhammad Afif Ridwansyah, Achmad Rizal, Sugondo HadiyosoAbstract:Abstrak Automatic Speech Recognition (ASR) adalah suatu sistem yang dapat mengenali, membandingkan dan mencocokkan pola suara masukan sistem tersebut dengan pola suara yang telah disimpan dalam memori. ASR terbagi menjadi dua jenis, yaitu Speech Recognition dan Speaker Recognition. Speaker Recognition adalah pengenalan identitas berdasarkan suara yang dikeluarkan (berupa intonasi suara, kedalaman suara, dan sebagainya). Pada penelitian ini dibangun sistem kunci berbasis suara dengan memanfaatkan Speaker Recognition. Pada penelitian ini digunakan metode Mel-Frequency Cepstral Coefficient (MFCC) sebagai ekstraksi ciri dan metode K-Nearest Neighbor (K-NN) sebagai klasifikasi ciri. Alat ini bekerja melalui dua tahapan, yaitu tahap pelatihan (training) dan tahap pengujian (testing). Hasil pengujian menunjukkan MFCC dan K-NN berhasil diimplementasikan dengan jumlah filterbank terbaik berjumlah 20 dan nilai koefisien terbaik sebanyak 13 koefisien dengan akurasi 100%. Hasil pengujian menunjukkan bahwa jumlah filterbank dan nilai koefisien mempengaruhi akurasi dari sistem. Kata kunci: Automatic Speech Recognition (ASR), biometrik suara, K-Nearest Neighbor (K-NN), MelFrequency Cepstral Coefficient (MFCC) . Abstract Automatic Speech Recognition (ASR) is a system that can identify, compare and match the system input voice patterns with the voice patterns that has been stored in memory. ASR is divided into two types, namely Speech Recognition and Speaker Recognition. Speaker Recognition is the introduction of the issued voice character (intonation of sound, depth of voice, etc.). The key system based on voice using Speaker Recognition was build in this study. In this research, the methods used were Mel-Frequency Cepstral Coefficient (MFCC) as feature extraction and K-Nearest Neighbor (K-NN) as characteristic classification. This tool worked through two stages, namely training stage and testing stage. The results showed that the MFCC and K-NN were successfully implemented with best filter bank at number 20 filter, best Coefficient value at 13 Coefficient with 100% accuracy. The results showed that filter bank and Coefficient affect the accuracy of the system. Keywords : Automatic Speech Recognition (ASR) , Biometric, K-Nearest Neighbor (K-NN), MelFrequency Cepstral Coefficient (MFCC).
Achmad Rizal - One of the best experts on this subject based on the ideXlab platform.
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rancang bangun kunci berbasis suara pada pintu pintar dengan menggunakan metode mel frequency Cepstral Coefficient mfcc dan k nearest neighbor k nn
eProceedings of Engineering, 2018Co-Authors: Muhammad Afif Ridwansyah, Achmad Rizal, Sugondo HadiyosoAbstract:Abstrak Automatic Speech Recognition (ASR) adalah suatu sistem yang dapat mengenali, membandingkan dan mencocokkan pola suara masukan sistem tersebut dengan pola suara yang telah disimpan dalam memori. ASR terbagi menjadi dua jenis, yaitu Speech Recognition dan Speaker Recognition. Speaker Recognition adalah pengenalan identitas berdasarkan suara yang dikeluarkan (berupa intonasi suara, kedalaman suara, dan sebagainya). Pada penelitian ini dibangun sistem kunci berbasis suara dengan memanfaatkan Speaker Recognition. Pada penelitian ini digunakan metode Mel-Frequency Cepstral Coefficient (MFCC) sebagai ekstraksi ciri dan metode K-Nearest Neighbor (K-NN) sebagai klasifikasi ciri. Alat ini bekerja melalui dua tahapan, yaitu tahap pelatihan (training) dan tahap pengujian (testing). Hasil pengujian menunjukkan MFCC dan K-NN berhasil diimplementasikan dengan jumlah filterbank terbaik berjumlah 20 dan nilai koefisien terbaik sebanyak 13 koefisien dengan akurasi 100%. Hasil pengujian menunjukkan bahwa jumlah filterbank dan nilai koefisien mempengaruhi akurasi dari sistem. Kata kunci: Automatic Speech Recognition (ASR), biometrik suara, K-Nearest Neighbor (K-NN), MelFrequency Cepstral Coefficient (MFCC) . Abstract Automatic Speech Recognition (ASR) is a system that can identify, compare and match the system input voice patterns with the voice patterns that has been stored in memory. ASR is divided into two types, namely Speech Recognition and Speaker Recognition. Speaker Recognition is the introduction of the issued voice character (intonation of sound, depth of voice, etc.). The key system based on voice using Speaker Recognition was build in this study. In this research, the methods used were Mel-Frequency Cepstral Coefficient (MFCC) as feature extraction and K-Nearest Neighbor (K-NN) as characteristic classification. This tool worked through two stages, namely training stage and testing stage. The results showed that the MFCC and K-NN were successfully implemented with best filter bank at number 20 filter, best Coefficient value at 13 Coefficient with 100% accuracy. The results showed that filter bank and Coefficient affect the accuracy of the system. Keywords : Automatic Speech Recognition (ASR) , Biometric, K-Nearest Neighbor (K-NN), MelFrequency Cepstral Coefficient (MFCC).
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implementasi kunci berbasis suara menggunakan metode mel frequency Cepstral Coefficient mfcc
eProceedings of Engineering, 2016Co-Authors: Muhammad Nashih Rabbani, Achmad Rizal, Fiky Yosef SuratmanAbstract:Pada dasarnya setiap individu menghasilkan suara yang berbeda-beda, walaupun seseorang dapat menirukan suara tersebut namun suara yang dihasilkan tidak identik dengan suara yang ditiru. Sistem biometrik adalah sistem untuk melakukan identifikasi dengan menganalisa karakteristik fisik dan perilaku. Tugas Akhir ini membuat suatu sistem keamanan suara berbasis mikro komputer yang diimplementasikan menjadi kunci. Tugas Akhir ini menggunakan metode MFCC sebagai ekstraksi ciri dan K-NN sebagai klasifikasi cirinya. Pada penelitian Tugas Akhir ini telah berhasil membuat sistem pengenalan pembicara dengan tingkat akurasi terbaik sebesar 87.5% dan 1.80277 detik dengan menggunakan K = 5 dalam implementasi pembuka kunci menggunakan suara. Kata kunci : Mel-Frequency Cepstral Coefficient (MFCC), K-Nearest Neighbor (K-NN), biometrik suara, kunci suara, Euclidean Distance.