A Step Toward AI Tools for Quality Control and Musicological Analysis of Digitized Analogue Recordings: Recognition of Audio Tape Equalizations
Contributo in Atti di convegno
Data di Pubblicazione:
2017
Abstract:
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.
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.
Tipologia CRIS:
04.01 - Contributo in atti di convegno
Keywords:
audio tape equalization, automatic recognition of physical carrier peculiarities, quality control tool for digitization process, artificial intelligence for musicological analysis
Elenco autori:
Micheloni, Edoardo; Pretto, Niccolo'; CANAZZA TARGON, Sergio
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Titolo del libro:
Proceedings of the 11th International Workshop on Artificial Intelligence for Cultural Heritage co-located with the 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017)
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