Comparison of Different Wavelet Sub-Band Features in the Classification of Indonesian Stop Consonants in CV Syllable Context - Kristomo, Domy; Hidayat, Risanuri; Soesanti, Indah (International Journal of Engineering and Technology, 2018)
In the research field of signal processing by using wavelet method there are some factors affecting the accuracy of recognition such as the selection of the sub-band parameter, the selection of suitable mother wavelet or coefficient, and the determination of decomposition level. This paper presents a comparative study of three wavelet-based sub-bands (WBSB) combined with the moving average energy (MAE) features for classification of Indonesian stop consonants in consonant-vowel (CV) context. Three different feature sets used in this study are the MAE of each different wavelet sub-band using mother wavelet of daubechies2. The first feature set is the MAE taken from standard wavelet packet (WP) sub-band at the 4th level of decomposition denoted as WBSB. Whereas the second and third feature sets are the MAE taken from WP which the sub-band is selected based on the previous research denoted as WBSB1 and WBSB2. For the classification of the stops sound signal after feature extraction process, two different classifiers were used, based on multi-layer perceptron and random forest. The experimental result showed that the performance rank of feature extraction method were WBSB, WBSB1, and WBSB2, respectively. Â Â