Magnetic Tape Recorder Dataset

This repository contains the datasets collected and used in the research project:

O. Mikkonen, A. Wright, E. Moliner and V. Välimäki, “Neural Modeling Of Magnetic Tape Recorders,”
in Proceedings of the International Conference on Digital Audio Effects (DAFx),
Copenhagen, Denmark, 4-7 September 2023.

A pre-print of the article is available in arXiv.
The code is open-source and published in GitHub.
The accompanying web page can be found from here.

Overview

The data is divided into various subsets, stored in separate directories. The data contains both toy data generated using a software emulation of a reel-to-reel tape recorder, as well as real data collected from a physical device. The various subsets can be used for training, validating, and testing neural network behavior, similarly as was done in the research article.

Toy and Real Data

The toy data was generated using CHOWTape, a physically modeled reel-to-reel tape recorder. The subsets generated with the software emulation are denoted with the string `CHOWTAPE`. Two variants of the toy data was produced: in the first variant, the fluctuating delay produced by the simulated tape transport was disabled, and in the second kind, the delay was enabled. The latter variants are denoted with the string `WOWFLUTTER`.

The real data is collected using an Akai 4000D reel-to-reel tape recorder. The corresponding subsets are denoted with the string `AKAI`. Two tape speeds were used during the recording: 3 3/4 IPS (inches per second) and 7 1/2 IPS, with the corresponding subsets denoted with '3.75IPS' and '7.5IPS' respectively. On top of this, two different brands of magnetic tape were used for capturing the datasets with different tape speeds: Maxell and Scotch, with the corresponding subsets denoted with 'MAXELL' and 'SCOTCH' respectively.

Directories

For training the models, a fraction of the inputs from SignalTrain LA2A Dataset was used. The training, validation, and testing can be replicated using the subsets:

• ReelToReel_Dataset_MiniPulse100_AKAI_*/                   (hysteretic nonlinearity, real data)
• ReelToReel_Dataset_Mini192kHzPulse100_AKAI_*/     (delay generator, real data)
• Silence_AKAI_*/                                                                    (noise generator, real data)
• ReelToReel_Dataset_MiniPulse100_CHOWTAPE*/        (hysteretic nonlinearity, toy data)
• ReelToReel_Dataset_MiniPulse100_CHOWTAPE_F[0.6]_SL[60]_TRAJECTORIES/ (delay generator, toy data)

For visualizing the model behavior, the following subsets can be used:

• LogSweepsContinuousPulse100_*/                                 (nonlinear magnitude responses)
• SinesFadedShortContinuousPulse100*/                         (magnetic hysteresis curves)

Directory structure

Each directory/subset is made of up of further subdirectories that are most often used to separate the training, validation and test sets from each other. Thus, a typical directory will look like the following:
```
[DIRECTORY_NAME]
├── Train
│   ├── input_x_.wav
│   ...
│   ├── target_x_.wav
│   ...
└── Val
│   ├── input_y_.wav
│   ...
│   ├── target_y_.wav
│   ...
├── Test
│   ├── input_z_.wav
│   ...
│   ├── target_z_.wav
│   ...
```

While not all of the audio is used for training purposes, all of the subsets share part of this structure to make the corresponding datasets compatible with the dataloader that was used.

The input and target files denoted with the same number `x`, e.g. `input_100_.wav` and `target_100_.wav` make up a pair, such that the target audio is the input audio processed with one of the used effects. In some of the cases, a third file named `trajectory_x_.npy` can be found, which consists of the corresponding pre-extracted delay trajectory in the `NumPy` binary file format.