Characterizing DS0-rate traffic using neural networks

Current in-service nonintrusive measurement devices (INMDs) measure speech and noise levels, speech echo path loss, and speech echo path delay. Work on extending these measurements to include the automatic characterization of traffic on DS0-rate lines, i.e., 64 kbit/s PCM in the public switched network, is described. The ability to correctly identify speech and various types of data traffic could be used to gather more accurate statistics of local exchange traffic and could eventually serve to differentially route or tariff calls. In laboratory experiments, neural networks are used to distinguish speech from modem traffic, as well as differentiating between modems speeds. Classification is made on 14-ms segments of the signal with an overall accuracy of over 98%. The two-layered nonlinear neural networks performed better than both a linear-discrimination technique and a template-matching technique.<
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