arXiv AI
PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors
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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
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 Machine Learning
Jul 7
Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types
arXiv:2607. 03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets.
arXiv AI
Jun 2
Project SPARROW and the Future of Conservation Technology
arXiv:2606. 00108v1 Announce Type: cross Abstract: Global biodiversity is declining at unprecedented rates, yet the tools available to monitor and protect ecosystems remain limited by constraints in power, connectivity, and accessibility.
arXiv AI
Jun 16
An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms
arXiv:2606. 16290v1 Announce Type: cross Abstract: Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardware constraints.
arXiv AI
Jul 14
ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification
arXiv:2607. 09680v1 Announce Type: cross Abstract: Continuous cardiac monitoring in wearable devices demands classifiers that are simultaneously accurate, energy-efficient, and deployable on resource-constrained hardware.