The Video-Mediated Communication Testbed was originally developed for inhouse use. We are in the process of making the tool publicly available as open-source.
The tool is programmed in python and is build using the following main libraries:
- GStreamer - for all the actual media handling. Accessing it via the python bindings for GObject Introspection
- Gtk - For all GUI related aspects.
- SleekXMPP - The open protocol Extensible Messaging and Presence Protocol (XMPP) is used for connection handling, chat messages and remote procedure calls
Right now the testbed is optimized to run on Linux. The testbed was also tested on Windows and MacOS X but smaller fixes and optimizations of the GStreamer pipelines is necessary. Extending it to mobile platforms is under consideration.
At the moment the VMC-TB has three main graphical components Client, ObserverControl and Player. Experiment setup is done via python scripts.
The video client for multiparty conferencing provides an example of how the client is configured during the conduction of an experiment. As shown, the client presents an overview of the test participants that are currently active in the experiment. The current task that the test participants have to discuss is integrated in the lower left corner of the interface.
The observer control client provides a GUI for the experiment conductor. Within this client the experiment conductor can dynamically join the conversation, see the status of the participants and set and execute the experiment procedure. Furthermore the experimental design (e.g. task set-up, manipulation of parameters) can be implemented and manually adjusted within this client.
With this tool sessions of conducted experiments can be viewed and analyzed. Various types of data scripts (e.g. speech pattern data, questionnaire data) can be processed and exported and used for further analysis. The tool provides an overview of the labeled speech pattern of each participant. Furthermore, the color denotes the type of identified speech activity. The experiment analyst also has the possibility to manually tag and categorize speech data.
In this component diagram a more detail view of the interplay of the different parts is given.