Utilisation of Wavelet Packet Transform for Speech Signal Coding
Keywords:
Compression, Decomposed, Signal energy, Wavelet Packet Decomposition (WPD), Wavelet Packet Transform (WPT), Zero coefficientsAbstract
In modern communication, effective speech coding is very important. This paper utilises wavelet packet transform (WPT) for speech signal coding. WPT extracts unique features from speech signals. The speech signal is decomposed into small segments, based on decomposition levels. The methodology utilises an audio compression speech technique for speech signals with less space occupation and bandwidth. The WPT method is effective as it takes into consideration different threshold levels, such as global dependent and level dependent. The evaluation of this method was conducted at different threshold levels, with different types of wavelets at different levels of decomposition, from lower to higher. The performance at each threshold is compared using the percentage of zero coefficients and retained signal energy, based on WPT and wavelet packet decomposition (WPD).
References
I. S. Fathi, M. A. A. Makhlouf, E. Osman, and M. A. Ahmed, “An energy-efficient compression algorithm of ECG signals in remote healthcare monitoring systems,” in IEEE Access, vol. 10, pp. 39129–39144, 2022, doi: https://doi.org/10.1109/access.2022.3166476
M. M. Valei and A. Mahloojifar, “ECG signal coding to improve compression for use in telemedicine,” Journal of Biomedical Engineering, vol. 7, no. 3, pp. 265–276, Nov. 2013, doi: https://doi.org/10.22041/ijbme.2013.13210
R. Benzid, F. Marir, A. Boussaad, M. Benyoucef, and D. Arar, “Fixed percentage of wavelet coefficients to be zeroed for ECG compression,” Electronics Letters, vol. 39, no. 11, pp. 830–831, May 2003, doi: https://doi.org/10.1049/el:20030560
S. Patel and A. Datar, “ECG data compression using wavelet transform,” International Journal of Engineering Trends and Technology, vol. 9, no. 3, pp. 770–776, 2014, doi: https://doi.org/10.14445/22315381/ijett-v9p346
K. Nguyen-Phi and H. Weinrichter, “ECG signal coding using wavelet transform and binary arithmetic coder,” Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications, vol. 3, pp. 1344–1348, doi: https://doi.org/10.1109/icics.1997.652208
K. Uyar and Y. Z. Ider, “Development of a compression algorithm suitable for exercise ECG data,” 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001, pp. 3521–3524, vol. 4, doi: https://doi.org/10.1109/iembs.2001.1019591
X. Wang and J. Meng, “A 2-D ECG compression algorithm based on wavelet transform and vector quantization,” Digital Signal Processing, vol. 18, no. 2, pp. 179–188, Mar. 2008, doi: https://doi.org/10.1016/j.dsp.2007.03.003
B. Sridharan and K. Thanushkodi, “A survey on coding algorithms in medical image compression,” International Journal on Computer Science and Engineering, vol. 2, no. 5, Aug. 2010, Available: https://www.researchgate.net/publication/49941906_A_Survey_On_Coding_
Algorithms_In_Medical_Image_Compression
G. K. Kharate, V. H. Patil, “Color image compression based on wavelet packet best tree,” International Journal of Computer Science Issues, vol. 7, no. 2, Mar. 2010, Available: https://arxiv.org/pdf/1004.3276
R. Vig and S. S. Chauhan, “Speech compression using multi-resolution hybrid wavelet using DCT and walsh transforms,” Procedia Computer Science, vol. 132, pp. 1404–1411, 2018, doi: https://doi.org/10.1016/j.procs.2018.05.070
S. J. Nikhil, “Image compression using lifting based wavetet transform,” International Journal of Engineering Research & Technology, vol 3, no. 2, Feb. 2014, Available: https://www.ijert.org/research/image-compression-using-lifting-based-wavetet-transform-IJERTV3IS20412.pdf