Omkar2118/datasets huggingface dataset:
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Continuous)

GUITAR-FX-DIST is a dataset of electric guitar recordings processed with overdrive, distortion, and fuzz audio effects. It was developed for research in guitar effects detection, classification, and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing, or effects modelling. It contains both unprocessed and processed recordings.

The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete


Authors:

Marco Comunità - Centre for Digital Music, Queen Mary University of London


Reference:

If you make use of GUITAR-FX-DIST, please cite the following publication:

```
@article{comunita2020guitar,
  title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks},
  author={Comunit{\`a}, Marco and Stowell, Dan and Reiss, Joshua D},
  journal={arXiv preprint arXiv:2012.03216},
  year={2020}
}
```

Introducing paper: Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Discrete)

GUITAR-FX-DIST is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.

The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete


Authors:

Marco Comunità - Centre for Digital Music, Queen Mary University of London


Reference:

If you make use of GUITAR-FX-DIST, please cite the following publication:
```
@article{comunita2020guitar,
  title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks},
  author={Comunit{\`a}, Marco and Stowell, Dan and Reiss, Joshua D},
  journal={arXiv preprint arXiv:2012.03216},
  year={2020}
}
```

Introducing paper: Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Discrete)

GUITAR-FX-DIST is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.

The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete


Authors:

Marco Comunità - Centre for Digital Music, Queen Mary University of London


Reference:

If you make use of GUITAR-FX-DIST, please cite the following publication:
```
@article{comunita2020guitar,
  title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks},
  author={Comunit{\`a}, Marco and Stowell, Dan and Reiss, Joshua D},
  journal={arXiv preprint arXiv:2012.03216},
  year={2020}
}
```

Introducing paper: Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Continuous)

GUITAR-FX-DIST is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.

The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete


Authors:

Marco Comunità - Centre for Digital Music, Queen Mary University of London


Reference:

If you make use of GUITAR-FX-DIST, please cite the following publication:

```
@article{comunita2020guitar,
  title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks},
  author={Comunit{\`a}, Marco and Stowell, Dan and Reiss, Joshua D},
  journal={arXiv preprint arXiv:2012.03216},
  year={2020}
}
```

Introducing paper: Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks
METAR
Full name: Meteorological Terminal Aviation Routine Dataset

Weather reports of 57 stations in the east coast.