arXiv AI
Jun 17
A Neuromorphic Trigger for Efficient Audio Event Detection
arXiv:2606. 17775v1 Announce Type: cross Abstract: Efficient processing of continuous audio streams remains a key challenge for real-time and resource-constrained systems.
Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring
The paper presents an autoencoder-based acoustic anomaly detection system implemented on Intel’s Loihi 2 neuromorphic processor. It achieves high detection performance—0.9959 AUC on a clean ToyADMOS ToyCar benchmark and 0.7990 source AUC on a noisy DCASE 2026 Task 2 ToyCar benchmark—while operating with only 0.0406–0.0426 mJ of dynamic energy per sample, far below CPU and GPU baselines. The system demonstrates that low‑power, on‑chip inference is feasible for persistent machine monitoring.
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arXiv AI
Sep 1
Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition
arXiv:2608.30792v1 Announce Type: cross Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificia...
arXiv AI
Aug 25
PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors
arXiv:2608.23101v1 Announce Type: cross Abstract: Recent progress in the field of TinyML has demonstrated that low-power hardware based on microcontrollers can achieve bird species monitoring in real...
arXiv Machine Learning
Jul 27
MemNMF: Memory-Augmented NMF on LPC Spectra for Anomalous Sound Detection
arXiv:2607. 22086v1 Announce Type: cross Abstract: Autoencoder-based anomalous sound detection is attractive for machine condition monitoring because it can be trained using only normal recordings and yields an interpretable anomaly score from reconstruction error.
arXiv Machine Learning
Jun 30
Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data
arXiv:2606. 29339v1 Announce Type: cross Abstract: Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring.
arXiv Machine Learning
Jun 18
Adaptive Speech-to-Spike Encoding for Spiking Neural Networks
arXiv:2606. 19039v1 Announce Type: cross Abstract: The mismatch between continuous acoustic signals and discrete event-driven processing remains a fundamental bottleneck for neuromorphic speech processing.
Hugging Face Trending Papers
Jun 28
Two kinds of robustness are not the same: disentangling fault tolerance and low-SNR robustness in multi-domain event detection on real data
Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise.
arXiv AI
Aug 24
Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring
The paper explores how the number of bird species (target classes) affects the compressibility of neural networks for passive acoustic monitoring on microcontroller units (MCUs). By training and compressing models with varying class counts, the authors show that significant compression can be achieved with minimal performance loss. They also benchmark different hardware platforms and assess the feasibility of deploying energy‑autonomous monitoring devices.
arXiv AI
Aug 12
HyWA: Architecture-Preserving Personalized Voice Activity Detection for Full-Duplex Voice Assistants
arXiv:2510. 12947v3 Announce Type: replace-cross Abstract: Voice activity detection (VAD) serves as an early gate in voice-assistant pipelines for smart devices.
arXiv Machine Learning
Sep 25
Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement
The paper presents a redesign of the LiSenNet speech‑enhancement model for deployment on the STM32N6570‑DK Neural‑ART microcontroller accelerator. By replacing the recurrent bottleneck with convolutional mixers, converting unsupported operations to static int8 primitives, and using bounded decoder activations, the authors achieve an NPU‑compatible model that matches or surpasses the original LiSenNet in quality (PESQ 3.08 vs 3.01 FP32) while running each 16 ms input hop in 4.83 ms (real‑time factor 0.30). The study demonstrates that co‑designing parameter count, operator compatibility, quantization range, and streaming state is essential for efficient real‑time speech enhancement on constrained NPUs.
arXiv AI
Jun 15
Giving AI a Headache: Acoustic Adversarial Attacks to Computer Vision Applications
arXiv:2606. 14658v1 Announce Type: cross Abstract: Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle control, facial recognition, and security cameras.
arXiv Computer Vision
Sep 3
Efficient Passive Acoustic Monitoring of Killer Whales Using a Two-Stage Detection and Ecotype Classification Cascade
The paper presents a lightweight ResNet-based two-stage cascade for passive acoustic monitoring of killer whales. First, it detects vocalizations, then it classifies confident detections into five eastern North Pacific ecotypes, abstaining on ambiguous calls. The pipeline achieves high macro‑F1 scores on the DCLDE 2027 dataset and improves real‑time inference speed, while active learning adapts the detector to new acoustic environments.