The audio signals can provide rich semantic cues for analyzing multimedia content, so audio information has been recently used for content-based multimedia indexing and retrieval. Due to growing amount of audio data, demand for efficient retrieval techniques is increasing. In this thesis work, we propose a complete, scalable and extensible audio based content management and retrieval system for news broadcasts. The proposed system considers classification, segmentation, analysis and retrieval of an audio stream. In the sound classification and segmentation stage, a sound stream is segmented by classifying each sub segment into silence, pure speech, music, environmental sound, speech over music, and speech over environmental sound in multiple steps. Support Vector Machines and Hidden Markov Models are employed for classification and these models are trained by using different sets of MPEG-7 features. In the analysis and retrieval stage, two alternatives exist for users to query audio data. The first of these isolates user from main acoustic classes by providing semantic domain based fuzzy classes. The latter offers users to query audio by giving an audio sample in order to find out the similar segments or by requesting expressive summary of the content directly. Additionally, a series of tests was conducted on audio tracks of TRECVID news broadcasts to evaluate the performance of the proposed solution.

Aquesta tesi tracta de cercadors d'audio basats en contingut. Específicament, tracta de desenvolupar tecnologies que permetin fer més estret l'interval semàntic o --semantic gap' que, a avui dia, limita l'ús massiu de motors de cerca basats en contingut. Els motors de cerca d'àudio fan servir metadades, en la gran majoria generada per editors, per a gestionar col.leccions d'àudio. Tot i ser una tasca àrdua i procliu a errors, l'anotació manual és la pràctica més habitual. Els mètodes basats en contingut àudio, és a dir, aquells algorismes que extreuen automàticament etiquetes descriptives de fitxers d'àudio, no són generalment suficientment madurs per a permetre una interacció semàntica. En la gran majoria, els mètodes basats en contingut treballen amb descriptors de baix nivell, mentre que els descriptors d'alt nivell estan més enllà de les possibilitats actuals. En la tesi explorem mètodes, que considerem pas previs per a atacar l'interval semàntic. / This dissertation is about audio content-based search. Specifically, it is on developing technologies for bridging the semantic gap that currently prevents wide-deployment of audio content-based search engines.Audio search 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 a user friendly representation for interacting with audio content. Mostly, content-based methods are based on low-level descriptions, while high-level or semantic descriptions are beyond current capabilities. In this thesis we explore technologies that can help close the semantic gap.