Human-in-the-Loop Efficiency Analysis for Binary Classification in Edyson

Edyson is a human-in-the-loop (HITL) tool for browsing and annotating large amounts of audio data quickly. It builds on temporally disassembled audio and massively multi-component audio environments to overcome the cumbersome time constraints that come with linear exploration of large audio data. This study adds the following contributions to Edyson: 1) We add the new use case of HITL binary classification by sample; 2) We explore the new domain oceanic hydrophone recordings with whale song, along with speech activity detection in noisy audio; 3) We propose a repeatable method of analysing the efficiency of HITL in Edyson for binary classification, specifically designed to measure the return on human time spent in a given domain. We exemplify this method on two domains, and show that for a manageable initial cost in terms of HITL, it does differentiate between suitable and unsuitable domains for our new use case — a valuable insight when working with large collections of audio.