Implementation of an AI-based model for detecting speed variations by means of spectrogram images for new magnetic tapes preservation strategies

Old 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. This 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. Instead 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.