Low-power SVM classifiers for sound event classification on mobile devices

With the high processing power of today's smartphones, it becomes possible to turn a smartphone into a personal audio surveillance and monitoring system. Ideally, such a system should be able to detect and classify a variety of sound events 24 hours a day and trigger an emergence phone call or message once a specified sound event (e.g., screaming) occurs. To prolong battery life, it is important to trade off the detection accuracy against power consumption. This paper investigates the power consumption of different stages of a sound-event classification system, including segmentation, feature extraction, and SVM scoring. The performance and power consumption of various acoustic features and SVM kernels are compared. This paper advocates the notion of intrinsic complexity through which the scoring function of polynomial SVMs can be written in a matrix-vector-multiplication form so that the resulting complexity becomes independent of the number of support vectors. Results show that this intrinsic complexity can reduce the CPU utilization of polynomial SVMs by 28 times without reducing classification accuracy.