Modeling of Audio Data for Multi-Criteria Query Formulation
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Date
2007-07
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Addis Ababa University
Abstract
The amount of available audio data in multimedia databases is increasing rapidly in consequence
of advancements in media creation, storage and compression technologies. This rapid increase
imposes new demands in audio data management and retrieval. As a result, growing number of
research works have been put into the area, such as audio indexing, classification and
segmentation. However, modeling and retrieval techniques are not adequate and handling audio
data content is still far from sufficient for most retrieval tasks.
This work proposes an audio data and audio repository model to fulfill user requirements in
retrieving audio data from large collections. The audio data repository model we proposed in this
thesis enable us to capture the audio itself, its low-level representation, all alphanumeric data
associated to the audio as well as timing information. Thus, it facilitates both keyword-based and
similarity-based operations on audio objects. In the proposed model, a generic audio repository
model that can handle a general audio as well as a sub-repository model (speech repository
model) that can manipulate speech through its constituent units is discussed. The speech
repository model enable us to capture all relevant information associated to speech units, to keep
track of hierarchical relationships between speech units and the speech that contains them. The
Object Relational scheme is used to manage these audio related data under a DBMS.
The proposed work augments audio retrieval by enabling users to apply semantic concepts (highlevel
features) linked to audio signals in their query in order to match relevant audio in a
database. Such an approach improves simple matching based on audio signal characteristics (lowlevel
features), as the user need not have example sounds for querying. In addition, an example
audio can be used as a query since users may not always know exact search terms to achieve
useful results.
Finally, the practicality of the proposed model is demonstrated by taking sample application areas
from the medical domain.
Keywords: low-level features, high-level features, audio data model, audio repository model,
content-based audio retrieval, keyword-based audio retrieval, Query-By-Example (QBE),
ADMMA.
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Keywords
Low-Level Features, High-Level Features, Audio Data Model, Audio Repository Model, Content-Based Audio Retrieval, Keyword-Based Audio Retrieval, Query-By-Example (Qbe), Admma