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This document will document progress, ideas and source code for dark data extraction systems. These systems use statistical inference to perform data extraction, integration and cleaning from unstructured/"dark" sources (forum posts, webpages, etc.). Data programming is the predominant paradigm for dark data extraction: noisy/conflicting user-defined functions are supplied to a generative model, which can recover the parameters of labelling process. Wherever possible, my projects are based on Snorkel/DeepDive.

Ideas (Extensions for the system):

• There isn't any work on Domain Specific primitives (DSPs) for audio data. Pre-trained audio models (VGGish) can serve as feature extractors for high-level concepts like emotion, accent and personality for speech data(WaveNet paper mentions that these are possible), musical genre (Sander Dieleman's Spotify CNN blog post), etc.

Ideas (Applications):

• Ecological/Environmental monitoring: use audio DSPs for building models of migration, logging/poaching, etc.

https://github.com/ganesh-srinivas/laughter/

UPDATE: This project was deemed successful, and I received a very positive evaluation from my mentors! :-) (you can view it at http://ganesh-srinivas.github.io/gsoc_final_evaluation.pdf)

The main deliverables from this project are machine learning classifiers that can perform laughter detection and categorization: identify if an audio clip contains laughter or not, and categorize the laughter (giggle, baby laugh, chuckle/chortle, snicker, belly laugh).