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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Characterizing Acoustic Environments with Olaf and Elsa
Committee:
Dr. David Anderson, ECE, Chair , Advisor
Dr. Mark Davenport, ECE
Dr. Marilyn Wolf, ECE
Dr. Aaron Lanterman, ECE
Dr. Wayne Daley, GTRI
Dr. Polo Chau, CoC
Abstract:
The confluence of signal processing and machine learning has created many innovative technologies in popular research areas such as speech recognition. However, many of the most successful methods are difficult to apply in areas that lack institutional support for research and creation of labeled data corpora. The focus of this work is the development of signal processing and machine learning methods that can be practically implemented with less human effort, less need for large quantities of labeled data, and less computational cost. Toward this goal, we have developed methods for outlier learning using augmented frozen dictionaries (OLAF) and estimating the likelihood of sparse approximations (ELSA) in the context of monitoring acoustic environments. Both methods utilize sparse, dictionary-based representations to capture information about the structure of the data and have been tested for monitoring poultry production facilities. They have proven effective in characterizing these environments and highlighting events or changes in the conditions present despite high levels of noise. These tools have potential to help producers to better understand the effects different practices have on the animals and could lead to better animal well-being.
