Identification and Restoration of LZ77 Compressed Data Using a Machine Learning Approach

Kwon, Beom; Gong, Myongsik; Huh, Jungwoo; Lee, Sanghoon

doi:10.23919/apsipa.2018.8659755

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…Подходы к идентификации и восстановлению структурированных данных на основе методов глубокого обучения описаны в [3][4][5]. Реконструкция звуковых сигналов, искаженных щелчками и потрескиваниями, методами сжатия и восстановления представлена в [6].…”

Section: анализ состояния предметной областиunclassified

Method for Restoring Audio Signal Using Convolutional Neural Networks

Morozova

Rakitskiy

2023

jour

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This paper describes a research of the restoring distorted audio signal possibility. Based on the previously obtained results of using deep machine learning methods, the concept of a neural network to correct a distorted audio signal has been developed. On the basis of the originally obtained results, several new neural network architectures were developed, focused on the audio signal restoring. The paper contains descriptions of the developed architectures with a theoretical substantiation of the possibility of their application. The presented architectures were tested to solve the problem of restoring the part of a specifc instrument in a musical composition where it was removed. The results of testing the developed architectures of neural networks are presented in several forms.

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Section: анализ состояния предметной областиunclassified

Method for Restoring Audio Signal Using Convolutional Neural Networks

Morozova

Rakitskiy

2023

jour

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“…To address this issue, in our previous work [5], we proposed a method that enables blind classification of the tuple length by using a support vector machine (SVM) without the header information. To extract features from one-dimensional (1-D) LZ77-compressed data, we used two statistical tests: Frequency Test and Runs Test in [6] that calculate the proportion of zeros and ones and the total number of uninterrupted sequences of identical bits over LZ77-compressed data, respectively.…”

Section: Introductionmentioning

confidence: 99%

Accurate Blind Lempel-Ziv-77 Parameter Estimation via 1-D to 2-D Data Conversion Over Convolutional Neural Network

2020

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The data sequence compressed using the Lempel-Ziv-77 (LZ77) algorithm comprises a series of fixed-length tuples. To decompress this data, it is essential to know the encoding parameters such as the tuple length used for the compression. If this essential information becomes unavailable owing to conditions such as loss of the header, it is difficult to accurately determine the parameters using the compressed data. In this study, we investigate the blind estimation of tuple length from the LZ77-compressed data when the header is unavailable. To this end, we propose a novel idea of utilizing the LZ77 image generated from the LZ77-compressed data. The LZ77 image exhibits unique patterns based on the image size. The correlation between the image size and tuple length is indicated by different patterns of vertical lines in the LZ77 image. By exploiting a convolutional neural network (CNN), we develop an iterative algorithm while generating LZ77 images with different size. The results of the experiment on a public database show that the LZ77 image plays the role of an extremely powerful visual feature descriptor, and the proposed iterative algorithm estimates the tuple length with 100% accuracy.

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