ANALISIS SISTEM PENGENAL SUARA SEPEDA MOTOR
MENGGUNAKAN SPEKTOGRAM
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Institusion
INSTITUT TEKNOLOGI DIRGANTARA ADISUTJIPTO
Author
ALIFAH SALMIATI MUSA, NUR
Subject
T Technology (General)
Datestamp
2024-03-27 05:36:46
Abstract :
Technology is developing rapidly to help humans fulfill all their needs. One of the emerging technologies is voice signal processing. Voice signal processing has been widely used in various fields such as voice processing, music, biomedical, navigation, and telecommunications. In this study, a motor vehicle voice recognition system based on a spectrogram is designed to recognize a sound issued by a motor vehicle and to obtain system performance then expressed in percentage accuracy In this study, word recognition analysis will be carried out using the spectrogram method and Euclidean Distance classification. With the aim of using the recognition of sounds issued by motorized vehicles with the accuracy of the training data and test data as evidenced in the form of the minimum Euclidean Distance results from all trials. The results of the study indicate that the designed system can identify motor sounds with an average recognition accuracy of 93%. starting with the process of training data and test data from 15 times of testing the test data on 20 training data. Vehicle voice recognition is made using the spectrogram method and Euclidean Distance classification
Technology is developing rapidly to help humans fulfill all their needs. One of the emerging technologies is voice signal processing. Voice signal processing has been widely used in various fields such as voice processing, music, biomedical, navigation, and telecommunications. In this study, a motor vehicle voice recognition system based on a spectrogram is designed to recognize a sound issued by a motor vehicle and to obtain system performance then expressed in percentage accuracy In this study, word recognition analysis will be carried out using the spectrogram method and Euclidean Distance classification. With the aim of using the recognition of sounds issued by motorized vehicles with the accuracy of the training data and test data as evidenced in the form of the minimum Euclidean Distance results from all trials. The results of the study indicate that the designed system can identify motor sounds with an average recognition accuracy of 93%. starting with the process of training data and test data from 15 times of testing the test data on 20 training data. Vehicle voice recognition is made using the spectrogram method and Euclidean Distance classification
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