An embedded system for acoustic pattern recognition

Tschöpe, Constanze; Duckhorn, Frank; Richter, Christian; Bluthgen, Peter; Wolff, Matthias

doi:10.1109/icsens.2017.8234380

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Wake-up detectors are often employed in acoustic event recognition because acoustic signals contain a lot of easily extracted information [15][16][17]. Because of this, they have been utilized in many fields, including safety and security [5,[18][19][20][21], biomedical and health monitoring [22][23][24], environmental monitoring [12,[25][26][27], Internet of Things (IoT) applications [2,8], structural health monitoring, non-destructive testing and machinery diagnosis [24], speech or voice activity detection [24,[28][29][30][31][32][33], and others.…”

Section: Introductionmentioning

confidence: 99%

Features and Always-On Wake-Up Detectors for Sparse Acoustic Event Detection

2022

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The need to understand and manage our surroundings has led to increased interest in sensor networks for the continuous monitoring of events and processes of interest. To reduce the power consumption required for continuous monitoring, dedicated always-on wake-up detectors have been designed, with an emphasis on their low power consumption, simple and robust design, and reliable and accurate detection. An especially interesting application of these wake‑up detectors is in detecting acoustic signals. In this paper, we present a study on the features and detectors applicable for the detection of sporadic acoustic events. We perform a state-of-the-art acoustic detector analysis, grouping the detectors based on the features they utilize and their implementations. This analysis shows that acoustic wake-up detectors predominantly utilize spectro-temporal (56%) and temporal features (36%). Following the state-of-the-art analysis, we select two detector architecture candidates for a case study on passing motor vehicle detection. We utilize our previously developed spectro-temporal decomposition detector and develop a novel level-crossing rate detector. The results of the case study shows that the proposed level‑crossing rate detector has lower component count (44 compared to 70) and power consumption (9.1 µW compared to 34.6 µW) and is an optimal solution for SNRs over 0 dB.

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