Pattern recognition by audio representation of multivariate analytical data

We present an objective recipe, based on the statistical distribution of data on each axis in k-dimensional space, for audio display of multivariate analytical data. Each measurement in the data vector is translated into an independent property of sound. Scaling is performed in a continuous manner. The case for k less than or equal to 9 is treated here, but it should be possible to approach k = 20 with slight loss of resolution and with somewhat increased risk of non-orthogonality. A significant feature of such a presentation of analytical data is that good acoustical standards are available so that the trained ear can associate each sound with a good estimate of the value in the original measurement. Excellent results were obtained when this method was applied to the pattern recognition of a test data set. Advantages over visual (graphical) representation schemes are discussed.