mlsanityData to 14 Oct 2026

A Neuromorphic Trigger for Efficient Audio Event Detection

arXiv 2606.17775 ↗First posted 16 Jun 2026cs.SD cs.AI cs.NEcited by 0

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Abstract (arXiv)

Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems. This paper introduces a neuromorphic trigger for audio event detection, based on a spiking neural network (SNN) that selectively gates input to downstream models. The proposed neuromorphic trigger acts as a flexible low-cost front-end, identifying salient audio segments and enabling these to be processed by a more computationally intensive model for tasks such as classification. The trigger is implemented as a lightweight fully connected SNN using a close-open filter for postprocessing, and is evaluated on two representative tasks: Anomalous Sound Detection (ASD) and Sound Event Detection (SED). For ASD, the trigger achieves a one-second segment-based F1 score of 0.97 on a class-agnostic form of the URBAN-SED dataset, demonstrating high reliability in identifying relevant audio regions. For SED, the trigger is combined with the Dang classifier on the DCASE 2017 Challenge Task 2 dataset, showing a potential 42.6× reduction in FLOPs while reducing the lower bound of the event-based error rate from 0.41 to 0.25. These results highlight the potential of neuromorphic triggers as real-time, energy-efficient front-end filters, enabling substantial reductions in computational cost.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 44 references that are in the map. First posted on arXiv 16 Jun 2026.

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15 of 44 references.

References · 15

  1. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 2,031
  2. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  3. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  4. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 177
  5. 2022Compute Trends Across Three Eras of Machine Learningcited by 163
  6. 2021Deep Spoken Keyword Spotting: An Overviewcited by 43
  7. 2022An Analytical Estimation of Spiking Neural Networks Energy Efficiencycited by 38
  8. 2018Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardwarecited by 37
  9. 2021Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networkscited by 14
  10. 2021Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devicescited by 10
  11. 2021You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detectioncited by 10
  12. 2021Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detectioncited by 7
3 more
  1. 2017Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Featurescited by 3
  2. 2017DNN and CNN with Weighted and Multi-task Loss Functions for Audio Event Detectioncited by 3
  3. 2024LEAD Dataset: How Can Labels for Sound Event Detection Vary Depending on Annotators?cited by 2

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