Audio magnetic tapes manufactured using polyester urethane are known to become nonplayable over time due to the degradation of the magnetic layer. Attempting to play degraded tapes to digitize them can cause extensive damage to the tape as well as to the play back device. For this reason, most of the magnetic tapes in cultural heritage institutions are in critical state. The purpose of our study is to preserve historical recordings in magnetic tapes by developing a nondestructive technique to determine degradation status. Our approach is to combine attenuated total reflectance Fourier transform infrared spectroscopy (ATR FT‐IR) with chemometric techniques, especially neural networks and least absolute shrinkage and selection operator (Lasso). The model built using neural networking was able to successfully classify playable and nonplayable with 97% to 98% accuracy when similar tape brands/models were in the training and the test set. With different brands/models in the test set, neural network model performed poorly. However, Lasso showed 95.5% accuracy for similar brand/models and 80.5% accuracy for different tape brands/models. This suggests that Lasso is the better technique to determine if a tape is degraded or not.

中文翻译：

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用神经网络和套索检测磁带退化

众所周知，由于磁性层的退化，使用聚酯氨基甲酸酯制造的音频磁带会随着时间的推移变得不可播放。尝试播放质量下降的磁带以将其数字化可能会对磁带和播放设备造成严重损坏。为此，文化遗产机构中的大部分磁带都处于危急状态。我们研究的目的是通过开发一种确定退化状态的无损技术来保存磁带中的历史记录。我们的方法是将衰减全反射傅里叶变换红外光谱 (ATR FT-IR) 与化学计量技术相结合，尤其是神经网络和最小绝对收缩和选择算子 (Lasso)。当类似的磁带品牌/型号在训练和测试集中时，使用神经网络构建的模型能够以 97% 到 98% 的准确率成功地对可播放和不可播放进行分类。由于测试集中的品牌/型号不同，神经网络模型表现不佳。但是，Lasso 对类似品牌/型号的准确度为 95.5%，对不同的胶带品牌/型号的准确度为 80.5%。这表明套索是确定磁带是否退化的更好技术。