DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging
Authors: Matthieu SIMEONI, Sepand Kashani, Paul Hurley, Martin Vetterli
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our real-data experiments show Deep Wave has similar computational speed to the state-of-the-art delay-and-sum imager with vastly superior resolution. While developed primarily for acoustic cameras, Deep Wave could easily be adapted to neighbouring signal processing fields, such as radio astronomy, radar and sonar. |
| Researcher Affiliation | Collaboration | Matthieu Simeoni IBM Zurich Research Laboratory meo@zurich.ibm.com Sepand Kashani École Polytechnique Fédérale de Lausanne (EPFL) sepand.kashani@epfl.ch Paul Hurley Western Sydney University paul.hurley@westernsydney.edu.au Martin Vetterli École Polytechnique Fédérale de Lausanne (EPFL) martin.vetterli@epfl.ch |
| Pseudocode | Yes | Algorithm 1 Deep Wave forward propagation |
| Open Source Code | Yes | Deep Wave implementation can be found on https://github.com/imagingofthings/Deep Wave. |
| Open Datasets | Yes | Finally we express our gratitude towards Robin Scheibler and Hanjie Pan for their openly-accessible real-world datasets [36, 43]. |
| Dataset Splits | Yes | Deep Wave is trained by splitting the data points into a training and validation set (respectively 80% and 20% in size). |
| Hardware Specification | No | The paper mentions 'a general-purpose CPU' and 'a standard computing platform' but does not provide specific models of CPUs, GPUs, or other hardware components used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or library versions). |
| Experiment Setup | Yes | For each frequency band, we chose an architecture with 5 layers. Optimisation of (9) is carried out by stochastic gradient descent (SGD) with momentum acceleration [51]. Finally, we substitute the Re Lu activation function by a scaled rectified tanh to avoid the exploding gradient problem [39]. |