The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today's sound practitioners due to the imperfections embedded in the physics of the magnetic recording process. This paper proposes a method for digitally emulating this character using neural networks. The signal chain of the proposed system consists of three main components: the hysteretic nonlinearity and filtering jointly produced by the magnetic recording process as well as the record and playback amplifiers, the fluctuating delay originating from the tape transport, and the combined additive noise component from various electromagnetic origins. In our approach, the hysteretic nonlinear block is modeled using a recurrent neural network, while the delay trajectories and the noise component are generated using separate diffusion models, which employ U-net deep convolutional neural networks. According to the conducted objective evaluation, the proposed architecture faithfully captures the character of the magnetic tape recorder. The results of this study can be used to construct virtual replicas of vintage sound recording devices with applications in music production and audio antiquing tasks.

翻译：磁性录音介质（如开盘式录音机和盒式录音机）的声音，因其录音物理过程中固有的不完美特性，至今仍受到当今声音从业者的追捧。本文提出一种利用神经网络对这一特性进行数字仿真的方法。所提系统的信号链由三个主要组件构成：磁性录音过程以及录音与回放放大器共同产生的磁滞非线性与滤波效应；磁带传输引起的时变延迟；以及来自多种电磁来源的组合加性噪声分量。我们的方法中，磁滞非线性模块采用递归神经网络建模，而延迟轨迹与噪声分量则分别通过使用U-net深度卷积神经网络的扩散模型生成。根据客观评估结果，所提架构能忠实地捕捉磁带录音机的特性。本研究的结果可用于构建复古录音设备的虚拟复刻，在音乐制作和音频仿古处理中具有应用前景。