Feature extraction of the Indonesian phonemes using discrete wavelet and wavelet packet transform

Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy

“…Step 4: Calculate the EWT W f ε n, t , where the detail correlation coefficient W f ε n, t and the approximate correlation coefficient W f ε 0, t are, respectively, expressed as (5) and (6) W f ε n, t = f , ψ n = ∫ f τ ψ n τ − t dτ = f^ω ψ n ω ∨ (5) W f ε 0, t = f , ψ 1 = ∫ f τ ψ 1 τ − t dτ = f^ω ψ 1 ω ∨ (6) Step 5: Reconstruct the signal according to (7). The EWF components of the signal are shown in (8) and (9)…”

“…Feature extraction is a critical process in the analysis of transformer vibration signals, through which the different working conditions of transformer windings can be distinguished effectively. The common feature extraction methods include the Fourier transform [3], wavelet packet decomposition [4][5][6][7][8][9], Hilbert-Huang transform (HHT) [9][10][11][12][13], etc. Although these methods can achieve the feature extractions for vibration signals to a certain extent, there are still some deficiencies.…”

Wavelet Based Feature Extraction for The Indonesian CV Syllables Sound

“…However, the similar study only focused on the sounds of the Indonesian vowels [12] and phonemes [11]. This paper is a development of the previous studies which using the WT and the ED algorithm [11]- [12].…”

“…In [7], The ED was used to estimate the expected value of the possibly incomplete feature vectors. In [12] [11], the study was done for extracting the Indonesian phonemes by using DWT and Wavelet Packet Transform (WPT) at 2nd through 4th level of decomposition by using Haar as mother wavelet. The result of this study showed that the DWT is a method that is more efficient and effective in extracting the Indonesian phonemes compared with the WPT as shown by the effectiveness ratio of 60% versus 40% and efficiency ratio of 57% versus 43% [11].…”

“…In [12] [11], the study was done for extracting the Indonesian phonemes by using DWT and Wavelet Packet Transform (WPT) at 2nd through 4th level of decomposition by using Haar as mother wavelet. The result of this study showed that the DWT is a method that is more efficient and effective in extracting the Indonesian phonemes compared with the WPT as shown by the effectiveness ratio of 60% versus 40% and efficiency ratio of 57% versus 43% [11]. In the context of speech classification, there are several previous studies that combining feature extraction methods based on Wavelet [13]- [17], MFCC [16]- [18], LPC [16]- [17], LPCC [16], and the classifier methods such as MLP [13,18,19], HMM [20], GMM [19], and LDA [14,17].…”