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Aggression Based Audio Ranking : Annotation and Automatic Ranking.

Wiltenburg, Daan H. (2019) Aggression Based Audio Ranking : Annotation and Automatic Ranking.

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Abstract:In many places, public and private, aggression can be a big problem. Therefore, surveillance is essential. This paper describes the research in automatic audio ranking based on aggression. The research is two-fold: First the most efficient way of rank annotation is determined by comparing different methods. A combination of pair-wise comparison and binary insertion sort turns out to be the most time efficient approach, while at the same time yielding the strongest ranking. The second problem addressed in this research is in the field of machine learning and learning-to-rank. Two type of loss functions are compared. The first loss function is Mean Squared Error, which uses a continuous target between 0 and 1 to train a network making this a regression approach. The second loss function is the log-likelihood function, which takes an ordered list as target making this a list-wise approach. The regression method performs comparable to human annotators, with a Kendall’s Rank Correlation Coefficient of 0.8228, where the human annotators achieved a Kendall’s Rank Correlation Coefficient of 0.88.
Item Type:Essay (Master)
Clients:
Sound Intelligence, Amersfoort, Nederland
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science, 70 social sciences in general
Programme:Interaction Technology MSc (60030)
Link to this item:http://purl.utwente.nl/essays/79215
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