Jurnal Publikasi STMIK Pontianak

Penerapan Algoritma Levenberg-Marquadt dan Backpropagation Neural Network untuk Klasifikasi Suara Manusia

Abstrak

Voice recognition technology is currently experiencing growth, especially in the case of speech processing. Speech processing is a way to exctract the desired information from a voice signal. This study discusses the classification of human voice system male and female. Extract the characteristics of the voice signal in each frame time domain and frequency domain is to help simplify and speed calculations. The features for voice or other audio between Short Time Energy, Zero Crossing Rate, Spectral Centroid, and others. Test results show that the classification system the human voice using the backpropagation neural network and Levenberg-Marquadt algorithm to change matrix weight is very good because of the complexity and rapid calculation which is not too high. Database voice sample of 40 voices with the test data as much as 5 votes. The output of the system is the result of the classification that has been identified with a similarity value >= 0.5 for male and <0.5 as a female. Testing using artificial neural network produced an average success rate in voice classification amounted to 91%.

Keywords : Feature Extraction, Classification, Backpropagation, Levenberg-Marquadt Algorithm, Human Voice