scholarly journals Compact Recurrent Neural Networks for Acoustic Event Detection on Low-Energy Low-Complexity Platforms

2020 ◽  
Vol 14 (4) ◽  
pp. 654-664 ◽  
Author(s):  
Gianmarco Cerutti ◽  
Rahul Prasad ◽  
Alessio Brutti ◽  
Elisabetta Farella
Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5445
Author(s):  
Marko Gazivoda ◽  
Dinko Oletić ◽  
Carlo Trigona ◽  
Vedran Bilas

Analog hardware used for signal envelope extraction in low-power interfaces for acoustic event detection, owing to its low complexity and power consumption, suffers from low sensitivity and performs poorly under low signal to noise ratios (SNR) found in undersea environments. To overcome those problems, in this paper, we propose a novel passive electromechanical solution for the signal feature extraction in low frequency acoustic range (200–1000 Hz), in the form of a piezoelectric vibration transducer, and a rectifier with a mechanically switched inductor. A simulation study of the novel solution is presented, and a proof-of-concept device is developed and experimentally characterized. We demonstrate its applicability and show the advantages of the passive electromechanical device in comparison to the active electrical solution in terms of operation with lower input signals (<20 mV compared to 40 mV), and higher robustness in low SNR conditions (output voltage loss for −10 dB ≤ SNR < 40 dB of 1 mV, compared to 10 mV). In addition to the signal processing performance improvements, compared to our previous work, the utilization of the presented novel passive feature extractor would also decrease power consumption of a detector’s channel by over 76%, enabling life-time extension and/or increased quality of detection with larger number of channels. To the best of our knowledge, this is the first solution presented in the literature that demonstrates the possibility of using a passive electromechanical feature extractor in a low-power analog wake-up event detector interface.


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