[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128604-en":3,"doc-seo-128604-105":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},128604,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Implementation of an AI-based Model for Detecting Speed Variations by Means of Spectrogram Images for New Magnetic Tapes Preservation Strategies - Thesis Summary","Magnetic tapes used for old audio storage have become obsolete, yet the recorded material remains valuable and is threatened by degradation over time. This study addresses reproduction errors caused by using a playback speed different from the recording speed, which produces inaccurate signals and damages associated cultural heritage. Instead of direct time-domain audio analysis, the work computes spectrograms and applies convolutional neural networks in the frequency domain. Results indicate promising performance and justify further development.","DEPARTMENT OF INFORMATION ENGINEERING  \nMASTER’S DEGREE IN COMPUTER ENGINEERING  \nIMPLEMENTATION OF ANAI-BASED MODEL FOR DETECTING SPEED VARIATIONS BY MEANS OF SPECTROGRAM IMAGES FOR NEW MAGNETIC TAPES PRESERVATION  \nSTRATEGIES  \nSupervisor: Prof. Sergio Canazza Targon  \nco-Supervisors: dr. Alessandro Russo, dr. Matteo Spanio  \nGraduand: Lorenzo Lunardon  \nACADEMIC YEAR 2023-2024  \nAbstract  \nOld audio storing methods such as magnetic tapes have been obsolete for decades, now more than ever with the advent of digital formats. The material stored on those tapes, though, is still precious today as it was when it was recorded, and it is at risk of being lost to time, as the materials degrade. The obvious solution to this problem is to digitize these documents, which is a tedious and error-prone process for any technician.  \nThis study focuses on preventing one of those errors, which is reproducing the tape using a playback speed different from the recording speed, leading to a very inaccurate signal and a spoilage of the heritage associated to the original document.  \nInstead of analyzing the audio signal directly in the time domain, we opted to explore the frequency domain, computing the spectrograms of the tapes and analyzing them using convolutional neural networks. The approach gave promising results, and the research showed that this is a valid way, worth exploring further.  \nAcknowledgements  \nFirst and foremost, I would like to thank my family for allowing me to take on this strenuous academic path and being by my side for all bumps in the road. I don’t know if I would have reached the ending of it without their unrelenting support and belief in me, which I myself lacked many times.  \nI also want to express my gratitude to all the friends who have made this whole journey much more pleasurable, from the ones I’ve met in my hometown as a kid and have been with me all along, to the ones I’ve met in Erasmus or some random hostel in the middle of nowhere. I am thankful for the laughs we had together and wish for many more to come.  \nLast but not least, I would like to thank professor Sergio Canazza and Matteo and Alessandro at CSC for helping me write this thesis and letting me contribute to their amazing work.  \nAgain, thank you.  \nContents  \nAbstract I  \nAcknowledgements III  \n1 Introduction 1  \n1.1 Centro di Sonologia Computazionale .................. 2  \n1.2 MPAI ................................... 3  \n2 Problem and Solution 7  \n2.1 Irregularities in Playback and Recording Speed ............ 7  \n2.2 A Possible Solution ............................ 8  \n2.2.1 Spectrograms ........................... 8  \n2.2.2 Convolutional Neural Networks ................. 10  \n3 Dataset Creation 17  \n3.1 Audio Dataset ............................... 17  \n3.2 Spectrogram Dataset ........................... 18  \n4 Model Training 23  \n4.1 Network Configuration .......................... 23  \n4.2 Quality Measures ............................. 24  \n4.3 Comparing Input Sizes .......................... 25  \n4.4 Comparing Scales ............................. 27  \n4.4.1 Assessment ............................ 29  \n5 Testing 33  \n5.1 Test Dataset ................................ 33  \n5.2 Performance On Test Dataset ...................... 34  \n6 Implementation 37  \n6.1 Future Developments ........................... 39  \n7 Conclusion 41  \nAppendixes 45  \nA Code 45  \nA.1 Methods used for dividing spectrograms in half ............ 45  \nA.2 Script used for computing the spectrograms .............. 49  \nA.3 Main audio analyzer script ........................ 52  \nA.4 Jupyter Notebook for training the models ............... 56  \nA.5 Jupyter Notebook for testing the models ................ 63  \nList of Figures  \n1.1 Damaged and corrupted magnetic tapes ................ 3  \n1.2 MPAI AI Framework ........................... 4  \n1.3 ARP Framework ............................. 4  \n2.1 Spectrograms of the same sample with three different scales ..... 9  \n2.2 Mode","cbCaias6P3VOHw0t","https://ap.wps.com/l/cbCaias6P3VOHw0t","pdf",2447987,1,78,"English","en",105,"# Abstract\n# Acknowledgements\n# Introduction\n## Centro di Sonologia Computazionale\n## MPAI\n# Problem and Solution\n## Irregularities in Playback and Recording Speed\n## A Possible Solution\n### Spectrograms\n### Convolutional Neural Networks\n# Dataset Creation\n## Audio Dataset\n## Spectrogram Dataset\n# Model Training\n## Network Configuration\n## Quality Measures\n## Comparing Input Sizes\n## Comparing Scales\n### Assessment\n# Testing\n## Test Dataset\n## Performance On Test Dataset\n# Implementation\n## Future Developments\n# Conclusion\n# Appendixes\n## Code","[{\"question\":\"Why is playback speed mismatch a problem for magnetic tape reproduction?\",\"answer\":\"Reproducing a tape with a playback speed different from the recording speed leads to an inaccurate signal and causes spoilage of the heritage tied to the original document.\"},{\"question\":\"What approach does the study use instead of analyzing audio in the time domain?\",\"answer\":\"The study generates spectrograms for the tapes and analyzes them using convolutional neural networks in the frequency domain.\"},{\"question\":\"What overall outcome does the research report?\",\"answer\":\"The approach produced promising results, showing that spectrogram-based analysis with CNNs is a valid direction worth further exploration.\"}]","Implementation of an AI-based Model for Detecting Speed Variations by Means of Spectrogram Images for New Magnetic Tapes Preservation Strategies - 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