Lazaros Vrysis

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The task of general audio detection and segmentation based in means of machine learning is very popular and high-demanding procedure nowadays. Most relevant works in the last decade aim at modelling audio in order to conduct a semantics analysis and a high--level categorization. A generic strategy that would detect audio events as means of transitions from(More)
This paper investigates methods aiming at the automatic recognition and classification of discrete environmental sounds, for the purpose of subsequently applying these methods to the recognition of soundscapes. Research in audio recognition has traditionally focused on the domains of speech and music. Comparatively little research has been done towards(More)
Multimedia semantic analysis is a key element in managing the exponentially growing amount of produced multimedia content, available on the web and the social media. Towards this direction, a semantically enhanced Web-TV environment providing video-on-demand and simulcast streaming services, is proposed. The system offers content management and analysis(More)
The present paper focuses on high-accuracy block-based sub-pixel motion estimation utilizing a straightforward error minimization approach. In particular, the mathematics of bilinear interpolation are utilized for the selection of the candidate motion vectors that minimize the error criterion, by estimating local minima in the error surface with arbitrary(More)
In this paper, an audio-driven algorithm for the detection of speech and music events in multimedia content is introduced. The proposed approach is based on the hypothesis that short-time frame-level discrimination performance can be enhanced by identifying transition points between longer, semantically homogeneous segments of audio. In this context, a(More)
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