  Traffic Pumping attacks are a form of high-volume SPAM that target telephone
networks, defraud customers and squander telephony resources. One type of call
in these attacks is characterized by very low-amplitude signal levels, notably
below the auditory threshold. We propose a technique to classify so-called
"dead air" or "silent" SPAM calls based on features derived from factorizing
the caller audio spectrogram. We describe the algorithms for feature extraction
and classification as well as our data collection methods and production
performance on millions of calls per week.
