aXess: an authoring tool for XML content management

The use of XML for representation of eLearning-content and for automatic generation of different kinds of teaching media from this material is with all its advantages nowadays stateof-the-art. In the last years there have been numerous projects that leveraged XML-based production environments. At the end of the financial advancement the created materials have to be maintained with limited (human) resources. In the majority of cases this is only possible, if the authors care for their teaching material without extensive IT-support. From our point of view there has so far been a lack of intuitive usable XML editors. The prototype of such an XML editor “aXess” is introduced with the intention to encourage a broad discussion about the required features to manage the content of eLearning-materials.

Content Classification of Multimedia Documents using Partitions of Low-Level Features

Audio-visual documents obtained from German TV news are classified according to the IPTC topic categorization scheme. To this end usual text classification techniques are adapted to speech, video, and non-speech audio. For each of the three modalities word analogues are generated: sequences of syllables for speech, “video words” based on low level color features (color moments, color correlogram and color wavelet), and “audio words” based on low-level spectral features (spectral envelope and spectral flatness) for non-speech audio. Such audio and video words provide a means to represent the different modalities in a uniform way. The frequencies of the word analogues represent audio-visual documents: the standard bag-of-words approach. Support vector machines are used for supervised classification in a 1 vs. n setting. Classification based on speech outperforms all other single modalities. Combining speech with non-speech audio improves classification. Classification is further improved by supplementing speech and non-speech audio with video words. Optimal F-scores range between 62% and 94% corresponding to 50% - 84% above chance. The optimal combination of modalities depends on the category to be recognized. The construction of audio and video words from low-level features provide a good basis for the integration of speech, non-speech audio and video.