Distortion-Aware Query-by-Example for Environmental Sounds

There has been much recent progress in the technical infrastructure necessary to continuously characterize and archive all sounds that occur within a given space or human life. Efficient and intuitive access, however, remains a considerable challenge. In other domains, i.e., melody retrieval, query-by-example (QBE) has found considerable success in accessing music that matches a specific query. We propose an extension of the QBE paradigm to the broad class of natural and environmental sounds. These sounds occur frequently in continuous recordings, and are often difficult for humans to imitate. We utilize a probabilistic QBE scheme that is flexible in the presence of time, level, and scale distortions along with a clustering approach to efficiently organize and retrieve the archived audio. Experiments on a test database demonstrate accurate retrieval of archived sounds, whose relevance to example queries is determined by human users.