Electroacoustic music on analog magnetic tape is characterized by several carrier-related specificities that must be considered when creating a copy for digital preservation. The tape recorder needs to be set to the correct speed and equalization, and the magnetic tape could have some intentional or unintentional alterations. During both the creation and the musicological analysis of a digital preservation copy, the quality of the work may be affected by human inattention. This article presents a methodology based on neural networks to recognize and classify the alterations of a magnetic tape from the video of the tape as it passes in front of the tape recorder's playback head. Furthermore, some machine-learning techniques have been tested to recognize a tape's equalization from its background noise. The encouraging results open the way to innovative tools able to unburden audio technicians and musicologists from repetitive tasks and to improve the quality of their work.

中文翻译：

---

支持从模拟磁带中保存电声音乐的计算方法

模拟磁带上的电声音乐具有几个与载体相关的特性，在创建用于数字保存的副本时必须考虑这些特性。磁带录音机需要设置为正确的速度和均衡，磁带可能会有一些有意或无意的改动。在数字保存副本的创作和音乐学分析过程中，作品的质量可能会受到人为疏忽的影响。本文介绍了一种基于神经网络的方法，用于从磁带视频中识别和分类磁带在磁带录音机播放磁头前面经过时的变化。此外，一些机器学习技术已经过测试，可以从背景噪声中识别磁带的均衡。