Electroacoustic music on analog magnetic tape is characterized by several specificities related to the carrier that have to be considered during the creation of a digital preservation copy of a document. The tape recorder need to be setup with the correct speed and equalization; moreover, the magnetic tape could present some intentional or unintentional alterations. During both the creation and the musicological analysis of a digital preservation copy, the quality of the work could be affected by human attention. This paper presents a methodology based on neural networks able to recognize and classify the alterations of a magnetic tape from the video of the tape itself flowing in the head of the tape recorder. Furthermore, some machine learning techniques has been tested to recognize equalization of a tape from its background noise. The encouraging results open the way to innovative tools able to unburden audio technicians and musicologists from repetitive tasks and improve the quality of their works.

Computing Methodologies Supporting the Preservation of Electroacoustic Music from Analog Magnetic Tape

Niccolò Pretto
;
Carlo Fantozzi;Edoardo Micheloni;Valentina Burini;Sergio Canazza
2018

Abstract

Electroacoustic music on analog magnetic tape is characterized by several specificities related to the carrier that have to be considered during the creation of a digital preservation copy of a document. The tape recorder need to be setup with the correct speed and equalization; moreover, the magnetic tape could present some intentional or unintentional alterations. During both the creation and the musicological analysis of a digital preservation copy, the quality of the work could be affected by human attention. This paper presents a methodology based on neural networks able to recognize and classify the alterations of a magnetic tape from the video of the tape itself flowing in the head of the tape recorder. Furthermore, some machine learning techniques has been tested to recognize equalization of a tape from its background noise. The encouraging results open the way to innovative tools able to unburden audio technicians and musicologists from repetitive tasks and improve the quality of their works.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3290157
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