exing of iPod music files is an example. Lower-level examples include audio, image and video metadata descriptors that allow users to search for particular characteristics, an extension of present word and image search algorithms now ubiquitous on the World Wide Web. These descriptors also allow automated matching of images or video or voice or audio clips. Manual metadata depends on our ability to name or describe content. We may of course have forgotten what something is called - a tune, perhaps. If we can hum the tune, it is possible to match the temporal and harmonic patterns of the tune to a data base. This is an automated or at least memory-jogging naming process that results in the automated creation of metadata, putting a name to the tune. This may not realise direct application value (the application will probably be free) but provides an opportunity to realise indirect value (sheet music or audio download value). The example in Figure 10.1 will be visually recognisable to some because of the pattern combination of the time signature (3/4 waltz time) and the opening interval (a perfect fifth). If you just used the perfect fifth it could be a quite large number of tunes, Twinkle Twinkle Little Making Telecoms Work Moon River Mancini Mancini Trumpet in Bb Figure 10.1 Tune recognition. Star, Theme from 2001, Whisper Not, and many more. However, only one of these examples is a waltz (clue to the tune, think, River and Moon). Congratulations. You have just performed an inference algorithm based on temporal and harmonic pattern recognition. 10.4.2 Examples of Mobile Metadata Any of the metadata examples above are potentially accessible in a wide-area wireless en- vironment. However, they are not unique to wireless. Mobile metadata combines metadata value with mobility value. Mobile metadata is based on our ability to acquire and use spatial knowledge of where a user is at any given time combined with other inputs available to us. Spatial knowledge may be based on cell ID or observed time difference or GPS (mac