We present a generic approach that identifies and differentiates among signals for wide range of problems. Originally our algorithm was developed to detect the presence of a specific vehicle belonging to a certain class via the analysis of the acoustic signals emitted while it is moving. A crucial factor in having a successful detection (no false alarm) is to construct signatures built from characteristic features that enable to discriminate between the class of interest and the residual information such as background. We construct the signatures of certain classes by the distribution of the energies among blocks which consist of wavelet packet coefficients. We developed an efficient procedure for adaptive selection of the characteristic blocks. We modified the CART algorithm in order to utilize it to be a decision unit in our scheme. However, this technology, which has many algorithmic variations, can be used to solve a wide range of classification and detection problems which are based on acoustic processing and, more generally, for classification and detection of signals which have near-periodic structure. We present results of successful application of the properly modified algorithm to detection of early symptoms of arterial hypertension in children via real-time analysis of pulse signals.
|Number of pages||8|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - 2001|