Duration-Aware Sound Event Detection on Ultra-Low-Power Sensor Devices

Sound event detection (SED) based on on-device machine learning (ML) presents considerable energy challenges for ultra-low-power sensor devices. In this paper, we propose DASH, a duration-aware SED system designed for energy-constrained sensor devices in domestic environments. As repeated inferences for continuous sound events lead to unnecessary energy consumption, DASH aims to minimize unnecessary inferences by predicting the duration of sound events. However, the variability of sound event durations across different environments and scenarios poses a major challenge in developing a responsive yet energy-efficient duration-aware SED system. To address this, DASH introduces three key solutions: (1) N-probability distribution-based event duration prediction, which identifies checkpoints where new inferences are likely needed; (2) Affinity-guided event classification, which performs low-energy affinity matching at checkpoints to determine whether ML inference is necessary; and (3) Interrupt blocking-enabling cycle-based device state control, which periodically checks for event presence with minimal energy consumption at non-checkpoint times. We implemented DASH on MSP430-based sensor devices deployed in real home environments. Experimental results demonstrate that DASH reduced energy consumption by approximately 97–98% compared to evaluation baselines, with only a 4.7% error rate.