Semantic storyboard of judicial debates: a novel multimedia summarization environment

Abstract

Purpose -The need of tools for content analysis, information extraction and retrieval of multimedia objects in their native form is strongly emphasized into the judicial domain: digital videos represent a fundamental informative source of events occurring during judicial proceedings that should be stored, organized and retrieved in short time and with low cost. This paper seeks to address these issues. Design/methodology/approach -In this context the JUMAS system, stem from the homonymous European Project (www.jumasproject.eu), takes up the challenge of exploiting semantics and machine learning techniques towards a better usability of multimedia judicial folders. Findings -In this paper one of the most challenging issues addressed by the JUMAS project is described: extracting meaningful abstracts of given judicial debates in order to efficiently access salient contents. In particular, the authors present an ontology enhanced multimedia summarization environment able to derive a synthetic representation of judicial media contents by a limited loss of meaningful information while overcoming the information overload problem. Originality/value -The adoption of ontology-based query expansion has made it possible to improve the performance of multimedia summarization algorithms with respect to the traditional approaches based on statistics. The effectiveness of the proposed approach has been evaluated on real media contents, highlighting a good potential for extracting key events in the challenging area of judicial proceedings.

Improved Expectation-Maximization Algorithm for Unknown Reverberant Audio-Source Separation

“…Additionally, the IS-NMF technique was used to generate the starting value for the NMF parameter [9]. We employ the fundamental truth parameter in the simulation, as it has been used in numerous comparable research' experiments [10]. The paper's primary contribution is to merge the TDOA estimating technique with the EM algorithm, eliminating the ambiguity of source permutations while addressing the EM algorithm's starting value issue.…”

“…As a result, our suggested technique attempts to integrate the NMF and TDOA predictions in order to minimize time consumption associated with the EM algorithm's starting values selection. Finally, we compare our proposed algorithm's separation quality to that of advanced approaches.Meanwhile, we consider the defined cases and compare with the blind separation algorithm [9,10,16]. In the underdetermined case, we compare with the algorithm [11,12], and the source separation performance is improved according to the simulation results.…”