{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/91186"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/91186","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Revealing the Determinants of Acoustic Aesthetic Judgment Through Algorithmic","abstract":"This project represents an important first step in determining the fundamental aesthetically relevant features of sound. Though there has been much effort in revealing the features learned by a deep neural network (DNN) trained on visual data, little effort in applying these techniques to a network trained on audio data has been performed. Importantly, these efforts in the audio domain often impose strong biases about relevant features (e.g., musical structure). In this project, a DNN is trained to mimic the acoustic aesthetic judgment of a professional composer. 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