Low-complexity hybrid time-frequency audio signal pattern detection

In this paper, we present a low-complexity hybrid time-frequency approach for the detection of audio signal patterns identified by proper spectral signatures. The proposed detection algorithm evolves through two main processing phases, denoted as coarse and fine, respectively. The evolution between these two phases is described through a finite state machine (FSM) model. The use of different processing phases is expedient to reduce the computational complexity and, thus, the energy consumption. Our results show that the proposed approach allows the efficient detection of the presence of signals of interest. The performance of the proposed detection algorithm is first investigated using “ideal” audio signals recovered from publicly available databases and, then, experimental audio signals acquired with a commercial microphone.