Leveraging User Input and Feedback for Interactive Sound Event Detection and Annotation
Bongjun Kim · 2018
Tagging of environment audio events is essential in many areas. However, finding sound events and labeling them within a long audio file is tedious and time-consuming. Building an automatic recognition system using modern machine learning is often not feasible because it requires a large number of human-labeled training examples and it is not reliable enough for all uses. I propose interactive sound event detection to solve the issue by combining machine search with human tagging, specifically focusing on the effectiveness of various types of user-inputs to the interactive sound searching. The types of user inputs that I will explore include binary relevance feedback, segmentation, and vocal imitation. I expect that leveraging one or combination of these user inputs would help users find audio contents of interest quickly and accurately, even in the situation where there are not enough training examples for a typical automated system.