{'fraction_non_alphanumeric': 0.012115563839701771, 'fraction_numerical': 0.0, 'mean_word_length': 5.137142857142857, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Wavelet transform and signal denoising using Wavelet method

Over the last decade, a great progress has been made in the signal processing field. Especially new signal processing methods such as Wavelet Transform (WT) allowed researchers to solve diverse and complicated signal processing issues. The paper provides answers to several questions related to WT technique such as what WT is, how and why WT emerged, what WT types currently available. The main advantages like noise reduction and compression of WT are also explained in this study. A set of MATLAB experiments were carried out in order to illustrate the use of WT as a signal denoising tool. Analysis on different signals contaminated with noise are performed. Different types of thresholding and mother wavelets were applied and the outcome of the experiments indicate that Daubechies family along with the soft thresholding technique suited our application the most. The study proves that choosing the right thresholding technique and wavelet family is vital for the success of signal denoising applications.