Machine Learning

This dataset is very vast and contains Spanish tweets related to COVID-19. There are 18958 unique tweet-ids in the whole dataset that ranges from December 2019 till May 2020 . The keywords that have been used to crawl the tweets are 'corona',  ,  'covid ' , 'sarscov2 ',  'covid19', 'coronavirus '.  For getting the other 33 fields of data drop a mail at "avishekgarain@gmail.com". Code snippet is given in Documentation file. Sharing Twitter data other than Tweet ids publicly violates Twitter regulation policies.    

100 Speakers each consisting of 5 voice samples for training data and 1 voice sample for testing data. Total of 600 voice samples collected in different audio formats like mpeg, mp4, mp3, ogg etc. These samples were than preprocessed and converted into .wav format. Each voice sample has a time duration of 5-10 seconds due to different lengths tuning of parameters should be done before usage. Whole Dataset size is 600mb and duration is 1 hour 40 minutes. This dataset can be used for speech synthesis, speaker identification. speaker recognition, speech recogniton etc.

Depths to the various subsurface anomalies have been the primary interest in all the applications of magnetic methods of geophysical prospection. Depths to the subsurface geologic features of interest are more valuable and superior to all other properties in any correct subsurface geologic structural interpretations.

Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task.