[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123562-en":3,"doc-seo-123562-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123562,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","ARMAS - ACTIVE RECONSTRUCTION OF MISSING AUDIO SEGMENTS","Digital audio signal reconstruction of lost or corrupt segments is addressed using deep learning and complementary signal-processing ideas. The work argues that popular linear interpolation, phase coding, and tone insertion remain common, while little prior research combines dithering (halftoning), steganography, and machine-learning regressors. The proposed approach fuses steganography and noisy-latent side information with shallow models (Random Forest, SVR) and deep LSTM regression, then evaluates performance against SPAIN and autoregressive baselines using four metrics.","arXiv :2111 . 10891v2 [ ee ss .AS] 23 Nov 2021  \nARMAS: ACTIVE RECONSTRUCTION OF MISSING AUDIO SEGMENTS  \nSachin Pokharel 1, Muhammad Ali 1, Zohra Cheddad 2, Abbas Cheddad 1􀀃  \n1 Department of Computer Science, Blekinge Institute of Technology,  \n371 79, Karlskrona, Sweden.  \n2 Facult des Sciences Exactes, Dpartement de Mathmatiques, Universit Frres Mentouri I, Route de Ain El Bey, Constantine 25017, Algeria.  \nABSTRACT  \nDigital audio signal reconstruction of lost or corrupt segment using deep learning algorithms has been explored intensively in the recent years. Nevertheless, prior traditional methods with linear interpolation, phase coding and tone insertion techniques are still in vogue. However, we found no research work on the reconstruction of audio signals with the fusion of dithering, steganography, and machine learning regressors. Therefore, this paper proposes the combination of steganography, halftoning (dithering), and state-of-theart shallow (RF- Random Forest and SVR- Support Vector Regression) and deep learning (LSTM- Long Short-Term Memory) methods. The results (including comparison to the SPAIN and Autoregressive methods) are evaluated with four different metrics. The observations from the results show that the proposed solution is effective and can enhance thereconstruction of audio signals performed by the side information (noisy-latent representation) steganography provides. This work may trigger interest in the optimization of this approach and/or in transferring it to different domains (i.e., image reconstruction) .  \nIndex Terms— Audio Reconstruction, Halftoning, Steganography, Machine learning  \n1. INTRODUCTION  \nCorrupt audio files and lost audio transmission and signals are severe issues in several audio processing activities such as audio enhancement and restoration. For example, in different applications and music enhancement and restoration situations, gaps could occur for several seconds [1] . Audio signals reconstruction remains a fundamental challenge in machine learning and deep learning despite the remarkable recent development in neural networks [1] . Audio in-painting, audio interpolation/extrapolation, or waveform replacement have been referred to as the restoration of lost information in audio. The reconstruction aims to provide consistent and relevant information while  eliminating audible artifacts to keep  \n* Corresponding author: [abbas.cheddad@bth.se](abbas.cheddad@bth.se)  \nthe listener unaware of any occurring issues [2] . Active reconstruction can be considered as a preemptive security measure to allow for self-healing when part of an audio becomes corrupted. To this end and to thebest of our knowledge, we found no prior research work on active reconstruction of audio signals with the fusion of steganography (an information hiding technique), halftonning and machine learning (ML) models. The initial idea (without ML) was proposed in a PhD thesis as an application of steganography. The hiding strategy ofsteganography can be tailored to act as an intelligent streaming audio/video system that uses techniques to conceal transmission faults from the listener that are due to lost or delayed packets on wireless networks with bursty arrivals, thus, providing a disruption tolerant broadcasting channel [3] .  \n2. RELATED WORK  \nIn the work of Khan et al. [4], a modern neuro-evolution algorithm, Enhanced Cartesian Genetic Programming Evolved Artificial Neural Network (ECGPANN), was proposed by the authors as a predictor of the lost signal samples in real-time. The authors have trained and tested the algorithms on audio speech signal data and evaluated them on the music signal. A deep neural network (DNN)-based regression method was proposed in [5] for a packet loss concealment (PLC) algorithm to predict a missing frame's characteristics. Two other DNNs were developed for the training part by integrating the log-power spectra and phases based on the unsupervised pre-training and supervised fine-","cbCaio0EuNl7603V","https://ap.wps.com/l/cbCaio0EuNl7603V","pdf",3346314,1,6,"English","en",105,"# Abstract\n# Introduction\n# Related Work","[{\"question\":\"What problem does ARMAS address in audio processing?\",\"answer\":\"ARMAS targets reconstructing missing or corrupted audio segments and lost audio transmissions, aiming to restore information while reducing audible artifacts.\"},{\"question\":\"How does the proposed method combine steganography, dithering, and machine learning?\",\"answer\":\"It integrates steganography and halftoning (dithering) with regression models, including shallow learners (Random Forest, SVR) and deep LSTM, using side information from a noisy-latent representation.\"},{\"question\":\"How is the reconstruction performance evaluated?\",\"answer\":\"Results are compared with SPAIN and autoregressive approaches using four different metrics to assess reconstruction quality.\"}]","ARMAS - 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