Synthetic Dataset

doi:doi:10.57702/a7l4jgri

Synthetic Dataset

The dataset used in this work is a custom synthetic dataset generated using the liquid-dsp library, containing 600000 examples of each of 13.8 million examples, with SNRs selected uniformly at random between [-10dB, 20dB], FOs selected uniformly at random between [-10%, 10%] of sample rate, and all further signal generation parameters such as filtering parameters, symbol order, etc. as specified in the paper.

BibTex:

@dataset{Arya_Mazumdar_and_Barna_Saha_2024,
    abstract = {The dataset used in this work is a custom synthetic dataset generated using the liquid-dsp library, containing 600000 examples of each of 13.8 million examples, with SNRs selected uniformly at random between [-10dB, 20dB], FOs selected uniformly at random between [-10%, 10%] of sample rate, and all further signal generation parameters such as filtering parameters, symbol order, etc. as specified in the paper.},
    author = {Arya Mazumdar and Barna Saha},
    doi = {10.57702/a7l4jgri},
    institution = {No Organization},
    keyword = {'3D-controllable', 'Airbnb Dataset', 'Anomaly Detection', 'Dataset', 'Document Summarization', 'Financial Data', 'Forecasting', 'Generated Images', 'Image Classification', 'Inference', 'Information Retrieval', 'Inverse Rendering', 'Latent Variable Graphical Models', 'Machine Learning', 'Mahalanobis metric', 'Polarization', 'Polynomial function', 'Position Bias', 'Radio Frequency', 'Ranking Bias', 'Recommendation Systems', 'Signal Processing', 'Stable Attractors', 'Synthetic', 'Synthetic Data', 'Synthetic Dataset', 'Temporal Patterns', 'Text Generation', 'Time series', 'Topic Modeling', 'causal inference', 'chromosome segmentation', 'computer vision', 'crowdsourcing', 'entity resolution', 'face manipulation', 'face synthesis', 'haze removal', 'image dehazing', 'image quality assessment', 'image restoration', 'mutual information', 'no noise', 'normal response', 'perceptual metric learning', 'recommendation systems', 'selection bias', 'single image dehazing', 'synthetic data', 'synthetic dataset', 'triplet-based learning', 'user-item bipartite graph'},
    month = {dec},
    publisher = {TIB},
    title = {Synthetic Dataset},
    url = {https://service.tib.eu/ldmservice/dataset/synthetic-dataset},
    year = {2024}
}