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Found 4 papers, 2 papers with code
Deep Active Audio Feature Learning in Resource-Constrained Environments
The scarcity of labelled data makes training Deep Neural Network (DNN) models in bioacoustic applications challenging.
Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking
This approach is built upon a joint-detection-and-tracking framework that is extended by a set of domain discriminator modules integrating an adversarial training strategy in addition to the tracking loss.
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devices
These technologies must ultimately be brought directly to the edge to fully harness the power of deep learning for the IoT.
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices
Significant efforts are being invested to bring state-of-the-art classification and recognition to edge devices with extreme resource constraints (memory, speed, and lack of GPU support).
Ranked #18 on
Audio Classification
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