The amount of available audio data in multimedia databases is increasing rapidly in consequencernof advancements in media creation, storage and compression technologies. This rapid increasernimposes new demands in audio data management and retrieval. As a result, growing number ofrnresearch works have been put into the area, such as audio indexing, classification andrnsegmentation. However, modeling and retrieval techniques are not adequate and handling audiorndata content is still far from sufficient for most retrieval tasks.rnThis work proposes an audio data and audio repository model to fulfill user requirements inrnretrieving audio data from large collections. The audio data repository model we proposed in thisrnthesis enable us to capture the audio itself, its low-level representation, all alphanumeric datarnassociated to the audio as well as timing information. Thus, it facilitates both keyword-based andrnsimilarity-based operations on audio objects. In the proposed model, a generic audio repositoryrnmodel that can handle a general audio as well as a sub-repository model (speech repositoryrnmodel) that can manipulate speech through its constituent units is discussed. The speechrnrepository model enable us to capture all relevant information associated to speech units, to keeprntrack of hierarchical relationships between speech units and the speech that contains them. ThernObject Relational scheme is used to manage these audio related data under a DBMS.rnThe proposed work augments audio retrieval by enabling users to apply semantic concepts (highlevelrnfeatures) linked to audio signals in their query in order to match relevant audio in arndatabase. Such an approach improves simple matching based on audio signal characteristics (lowlevelrnfeatures), as the user need not have example sounds for querying. In addition, an examplernaudio can be used as a query since users may not always know exact search terms to achievernuseful results.rnFinally, the practicality of the proposed model is demonstrated by taking sample application areasrnfrom the medical domain.rnKeywords: low-level features, high-level features, audio data model, audio repository model,rncontent-based audio retrieval, keyword-based audio retrieval, Query-By-Example (QBE),rnADMMA.