Wavelet Packet Sub-band Cepstral Coefficient for Speaker Verification

Min, Hang; Wei, Guangcun; Xu, Yunfei; Zhang, Yanna

doi:10.1109/iaeac54830.2022.9929836

Cited by 1 publication

(1 citation statement)

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“…A group of time-frequency atoms with good localization in both time and frequency are produced by repeatedly applying a set of filters to the signal to create wavelet packets [9]. In order to give a more precise and in-depth examination of the signal frequency content, the generated wavelet packets might be subjected to Fourier Transform analysis.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Cabrel,

Mumanikidzwa,

Shen

et al. 2024

JST

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Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of nonstationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

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Section: Introductionmentioning

confidence: 99%