  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.
