Omkar2118/datasets huggingface dataset:
Unbalance Classification Using Vibration Data
Full name: Vibration Measurements on a Rotating Shaft at Different Unbalance Strengths

This dataset contains vibration data recorded on a rotating drive train. This drive train consists of an electronically commutated DC motor and a shaft driven by it, which passes through a roller bearing. With the help of a 3D-printed holder, unbalances with different weights and different radii were attached to the shaft. Besides the strength of the unbalances, the rotation speed of the motor was also varied. This dataset can be used to develop and test algorithms for the automatic detection of unbalances on drive trains. Datasets for 4 differently sized unbalances and for the unbalance-free case were recorded. The vibration data was recorded at a sampling rate of 4096 values per second. Datasets for development (ID "D[0-4]") as well as for evaluation (ID "E[0-4]") are available for each unbalance strength. The rotation speed was varied between approx. 630 and 2330 RPM in the development datasets and between approx. 1060 and 1900 RPM in the evaluation datasets. For each measurement of the development dataset there are approx. 107min of continuous measurement data available, for each measurement of the evaluation dataset 28min.

Introducing paper: Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration Data
Lakh Pianoroll Dataset

The Lakh Pianoroll Dataset (LPD) is a collection of 174,154
[multitrack pianorolls](https://salu133445.github.io/lakh-pianoroll-dataset/representation) derived from the
[Lakh MIDI Dataset](http://colinraffel.com/projects/lmd/) (LMD).

## Getting the dataset

We provide multiple subsets and versions of the dataset (see
[here](https://salu133445.github.io/lakh-pianoroll-dataset/comparisons)). The dataset is available [here](https://salu133445.github.io/lakh-pianoroll-dataset/dataset).

## Using LPD

The multitrack pianorolls in LPD are stored in a special format for efficient
I/O and to save space. We recommend to load the data with
[Pypianoroll](https://salu133445.github.io/pypianoroll/) (The dataset is created
using Pypianoroll v0.3.0.). See [here](https://salu133445.github.io/pypianoroll/save_load.html)
to learn how the data is stored and how to load the data properly.

## License

Lakh Pianoroll Dataset is a derivative of
[Lakh MIDI Dataset](http://colinraffel.com/projects/lmd/) by
[Colin Raffel](http://colinraffel.com), used under
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
Lakh Pianoroll Dataset is licensed under
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) by
[Hao-Wen Dong](https://salu133445.github.io) and
[Wen-Yi Hsiao](https://github.com/wayne391).

Please cite the following papers if you use Lakh Pianoroll Dataset in a
published work.

- Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang,
  "__MuseGAN: Multi-track Sequential Generative Adversarial Networks for
  Symbolic Music Generation and Accompaniment__,"
  in _Proceedings of the 32nd AAAI Conference on Artificial Intelligence_
  (AAAI), 2018.

- Colin Raffel,
  "__Learning-Based Methods for Comparing Sequences, with Applications to
  Audio-to-MIDI Alignment and Matching__,"
  _PhD Thesis_, 2016.

## Related projects

- [MuseGAN](https://salu133445.github.io/musegan/)
- [LeadSheetGAN](https://liuhaumin.github.io/LeadsheetArrangement/)

Introducing paper: MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment