A JAVA FRAMEWORK FOR DATA SONIFICATION AND 3 D GRAPHIC RENDERING

Data audification is the representation of data by means of sound signals (waveforms or melodies typically). Although most data analysis techniques are exclusively visual in nature, data presentation and exploration systems could benefit greatly from the addition of sonification capabilities. In addition to that, sonic representations are particularly useful when dealing with complex, high-dimensional data, or in data monitoring or analysis tasks where the main goal is the recognition of patterns and recurrent structures. The main goal of this paper is to briefly present the audification process as a mapping between a discrete data set and a discrete set of notes (we shall deal with MIDI representation), and look at a couple of different examples from two well distinguished fields, geophysics and linguistics (seismograms sonificaton and text sonification). Finally the paper will present another example of the mapping between data sets and other information, namely a 3D graphic image generation (rendered with POVRay) driven by the ASCII text. 1. ABOUT DATA AUDIFICATION Data audification is the representation of data by sound signals; it can be considered as the acoustic counterpart of data graphic visualization, a mathematical mapping of information from data sets to sounds. In the past twenty years the word audification has acquired a new meaning in the world of computer music, computer science, and auditory display application development. Data audification is currently used in several fields, for different purposes: science and engineering, education and training, in most of the cases to provide a quick and effective data analysis and interpretation tool [3]. Although most data analysis techniques are exclusively visual in nature (i.e. are based on the possibility of looking at graphical representations), data presentation and exploration systems could benefit greatly from the addition of sonification capabilities. In addition to that, sonic representations are particularly useful when dealing with complex, highdimensional data, or in data monitoring tasks where it is practically impossible to use the visual inspection. More interesting and intriguing aspects of data sonification concern the possibility of describing patterns or trends, through sound, which were hardly perceivable otherwise. Moreover in many cases human ears are used to discover slight changes in acoustic patterns. One example is the medicine who routinely applies acoustic data analysis when uses the stethoscope to listen to breath noise and heart tones. Audification may give information about the inner structure of the represented data using the power of an abstract description. Any kind of regularity in the original data set will be reflected to the aural signal generated by the audification algorithm. One of the most important challenges is finding the proper balance between the amount of information that can be converted into an audio signal and the effective capability of that sound to communicate meaningful information to a listener. 2. MIDI ”MELODISATION” OF A DISCRETE DATA SET The melodisation of a data set, i.e. the creation of a melody starting from a list of data is an interesting way to convert into aural signals, almost any kind of information. For the sake of simplicity we can imagine a set of m elements (a list or a discrete set of values) to be sonified. The sonification will provide a melody, a list of notes chosen among a set ofn notes, to be drawn onto a pentagram. Without loose of generality, we can consider a standard, numerical, coding convention for the (well tempered) notes to be represented. In particular we shall refer to the MIDI 1 code, according to which the central ”C” note corresponds to the integer 60, ”C#” is 61, ”D” is 62, and so on (any semitone shift will add or subtract one from that previous value). The lowest acceptable MIDI value is 0 and the higher is 127, so there are 128 possible notes to represent our data. From a mathematical point of view the MIDI melodisation (the set of possibile notes is 128, so n = 128) could be considered as a map between two discrete set,