Facilitating the Manual Annotation of Sounds When Using Large Taxonomies
This work addresses the problem of arduous manual annotation for researchers and practitioners in information retrieval, but it is incremental as it focuses on tool development without major methodological breakthroughs.
The paper tackles the difficulty of manually annotating diverse audio content using large hierarchical taxonomies by developing and evaluating two user-centered tools, with a qualitative analysis revealing key usability insights.
Properly annotated multimedia content is crucial for supporting advances in many Information Retrieval applications. It enables, for instance, the development of automatic tools for the annotation of large and diverse multimedia collections. In the context of everyday sounds and online collections, the content to describe is very diverse and involves many different types of concepts, often organised in large hierarchical structures called taxonomies. This makes the task of manually annotating content arduous. In this paper, we present our user-centered development of two tools for the manual annotation of audio content from a wide range of types. We conducted a preliminary evaluation of functional prototypes involving real users. The goal is to evaluate them in a real context, engage in discussions with users, and inspire new ideas. A qualitative analysis was carried out including usability questionnaires and semi-structured interviews. This revealed interesting aspects to consider when developing tools for the manual annotation of audio content with labels drawn from large hierarchical taxonomies.