In this paper, we present a low-complexity hybrid time-frequency approach for the detection of audio signal patterns by proper spectral signatures. The proposed detection algorithm evolves through two main processing phases, denoted as coarse and fine, respectively. The evolution through these two phases is described by a finite state machine 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 efficiency 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
In this paper, we present a low-complexity hybrid time-frequency approach for the detection of audio signal patterns by proper spectral signatures. The proposed detection algorithm evolves through two main processing phases, denoted as coarse and fine, respectively. The evolution through these two phases is described by a finite state machine 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 efficiency 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
Low-complexity hybrid time-frequency audio signal pattern detection / Marco, Martalo; Gianluigi, Ferrari; Malavenda, CLAUDIO SANTO. - In: IEEE SENSORS JOURNAL. - ISSN 1530-437X. - STAMPA. - 13:2(2013), pp. 501-509. [10.1109/jsen.2012.2219045]
Low-complexity hybrid time-frequency audio signal pattern detection
MALAVENDA, CLAUDIO SANTO
2013
Abstract
In this paper, we present a low-complexity hybrid time-frequency approach for the detection of audio signal patterns by proper spectral signatures. The proposed detection algorithm evolves through two main processing phases, denoted as coarse and fine, respectively. The evolution through these two phases is described by a finite state machine 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 efficiency 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 microphoneI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
