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Found 6 papers, 2 papers with code
Can Synthetic Data Boost the Training of Deep Acoustic Vehicle Counting Networks?
In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency.
Real-Time Acoustic Perception for Automotive Applications
On the algorithmic side, the I-SPOT Project aims to enable detecting, localizing and tracking environmental audio signals by jointly developing microphone array processing and deep learning techniques that specifically target automotive applications.
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks
The understanding of the surrounding environment plays a critical role in autonomous robotic systems, such as self-driving cars.
An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models
End-to-end performance estimation and measurement of deep neural network (DNN) systems become more important with increasing complexity of DNN systems consisting of hardware and software components.
Automated design of error-resilient and hardware-efficient deep neural networks
It is thus desirable to exploit optimization potential for error resilience and efficiency also at the algorithmic side, e. g., by optimizing the architecture of the DNN.
Efficient Stochastic Inference of Bitwise Deep Neural Networks
Recently published methods enable training of bitwise neural networks which allow reduced representation of down to a single bit per weight.
