Aplikasi Pengenalan Suara Alat Musik Betawi Menggunakan Metode Mel-Frequency Cepstral Coeffisient Berbasis Matlab
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Hidayat, M. Tessar
Praptini, Puji Catur Siswi
Pratama, M. Fadli
Praptini, Puji Catur Siswi
Pratama, M. Fadli
Subject
Teknik Informatika
Datestamp
2022-09-23 03:00:23
Abstract :
Voice recognition application is Voice Recognition which means the application can recognize sound / capture sound using the MFCC method. The Mel-frequency cepstral coefficient method is one of the methods used for speech recognition that converts the sound signal into several parameters. The author has the initiative to create a speech recognition application with Betawi musical instrument objects where later applications can be run by listening to the sounds of Betawi musical instruments that have been used as WAV files. The author takes audio samples only from 2 Betawi musical instruments where each Betawi musical instrument is given 3 audio samples which will be used as training data in the application so that the application can distinguish each sound frequency in the audio sample that will be used as test data and proceed with the MFCC process so that the application can guess the audio sample of what musical instrument is read by the application using the SVM and KNN algorithm as a calcification of the results of the method process. So the end result is the application can guess what musical instruments have been heard by the application
Voice recognition application is Voice Recognition which means the application can recognize sound / capture sound using the MFCC method. The Mel-frequency cepstral coefficient method is one of the methods used for speech recognition that converts the sound signal into several parameters. The author has the initiative to create a speech recognition application with Betawi musical instrument objects where later applications can be run by listening to the sounds of Betawi musical instruments that have been used as WAV files. The author takes audio samples only from 2 Betawi musical instruments where each Betawi musical instrument is given 3 audio samples which will be used as training data in the application so that the application can distinguish each sound frequency in the audio sample that will be used as test data and proceed with the MFCC process so that the application can guess the audio sample of what musical instrument is read by the application using the SVM and KNN algorithm as a calcification of the results of the method process. So the end result is the application can guess what musical instruments have been heard by the application
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