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This paper describes the development of a generated solution for classification and segmentation of broadcast news audio. 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 classes in multiple steps. Support Vector Machines and Hidden(More)
In this stirdv, we have developed a iveh-hased qiier?, engine called AudinCBR to enahle conrenr-bused arid semunlic queries 011 midito? data. The interface is ahle to uncwer the Qitery-by-E.rample (QBE) and te.rtrra1 qiieries us in truditional Information Retrieval (IR) s,vsterns. Tlie relevancef~edhack. which is missing in man,v similar .sv.stem.s. is also(More)
We present a novel algorithm for structural analysis of audio to detect repetitive patterns that are suitable for content-based audio information retrieval systems, since repetitive patterns can provide valuable information about the content of audio, such as a chorus or a concept. The Audio Spectrum Flatness (ASF) feature of the MPEG-7 standard, although(More)
Audio data contains several sounds and is an important source for multimedia applications. One of them is unstructured Environmental Sounds (also referred to as audio events) that have noise-like characteristics with flat spectrums. Therefore, in general, recognition methods applied for music and speech data are not appropriate for the Environmental Sounds.(More)
A typical content-based audio management system deals with three aspects namely audio segmentation and classification, audio analysis, and content-based retrieval of audio. In this paper, we integrate the three aspects of content-based audio management into a single framework and propose an efficient method for flexible querying and browsing of auditory(More)