The identification in real-time streaming data using plain template-matching algorithms is difficult due to the undefined frame position in the on-line data compared to the frame-based features' position of the templates. We have investigated the use of the short-time Fourier-spectrum, the short-time Walsh-rpectrum and the short-time signal energy and the ratio of tbe succeeding features from the frame-shift point of view. The last two features aim to improve calculation s p e d in larger set of records. For further simplifying opcrrtions in the comparison stage, a quantization step was applied to the spectrum values which resulted in temary-bgic time-frequency maps. This is also useful for eliminating the effects of non-extraordinary spectral-shape dltortions by utilizing the prominent parts of the spectrum. An rlgorithm was developed for selecting the most suitabk segment combination of the sound records to be monitored where tbe differences between a11 segment pairs are the l a m The method was applied to identify advertisements am t&e RealAudio broadcast of the Hungarian Radio.Znder terms-d t i m e detection, feature selection, sound stream m o~i t o r i 4

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This chapter presents a novel approach to search in shared audio file storages, such as P2P-based systems. The proposed method is based on the recognition of specific patterns in the audio contents in such a way to extend the searching possibility from the description based model to a content-based one.

This article presents a novel approach to search in shared audio file storages such as P2P based systems. The proposed method is based on the recognition of specific patterns in the audio contents in such a way extending the searching possibility from the description based model to the content based model. The importance of the real-time pattern recognition algorithms that are used on audio data for content-based searching in streaming media is rapidly growing (Liu, Wang, & Chen, 1998). The main problem of such algorithms is the optimal selection of the reference patterns (soundprints) used in the recognition procedure. The proposed method is based on distance maximization and is able to quickly choose the pattern that later will be used as reference by the pattern recognition algorithms (Richly, Kozma, Kovács, & Hosszú, 2001). The presented method called EMESE (experimental media-stream recognizer) is an important part of a lightweight content-searching method, which is suitable for the investigation of the networkwide shared file storages. The experimental measurement data shown in the article demonstrate the efficiency of the proposed procedure.