A Step Toward AI Tools for Quality Control and Musicological Analysis of Digitized Analogue Recordings: Recognition of Audio Tape Equalizations

Historical analogue audio documents are indissolubly linked to their physical carriers on which they are recorded. Because of their short life expectancy these documents have to be digitized. During this process, the document may be altered with the result that the digital copy is not reliable from the authenticity point of view. This happens because digitization process is not completely automatized and sometimes it is influenced by human subjective choices. Artificial intelligence can help operators to avoid errors, enhancing reliability and accuracy, and becoming the base for quality control tools. Furthermore, this kind of algorithms could be part of new instruments aiming to ease and to enrich musicological studies. This work focuses the attention on the equalization recognition problem in the audio tape recording field. The results presented in this paper, highlight that, using machine learning algorithms, is possible to recognize the pre-emphasis equalization used to record an audio tape.