This book is for audio information retrieval practitioners. It is about audio content-based search. Specifically, it is on exploring promising paths for bridging the semantic gap that currently prevents wide deployment of audio content-based search engines. Music search sound engines rely on metadata, mostly human generated, to manage collections of audio assets. Even though time-consuming and error-prone, human labeling is a common practice. Audio content-based methods, algorithms that automatically extract description from audio files, are generally not mature enough to provide the user friendly representation that users demand when interacting with audio content. This dissertation has two parts. In a first part we explore the strengths and limitation of a pure low-level audio description technique: audio fingerprinting. In the second part, we hypothesize that one of the problems that hinders the closing the semantic gap is the lack of intelligence that encodes common sense knowledge and that such a knowledge base is a primary step toward bridging the semantic gap. We present a sound effects retrieval system which leverages both low-level and semantic technologies.

Данное издание не является оригинальным. Книга печатается по технологии принт-он-деманд после получения заказа.