i can try to help you though, i worked as a deep learning engineer in computer vision:

1. do you mean the dimension of 1 sample is [2000, 5] ? that is a very weird shape for an image. usually they have a shape of [h, w, 3] and [h, w, 4] for video data - a temporal additional dimension is added
2. what do you want this model should be classifying ? so far it sounds more trivial - but depending on the object it might be a bit more complex.
3. the more complex your task -> more complex your model must be -> a larger data set you will need
4. how are the labels distributed in your data set ?
5. do you use adversarial attacks for robustness ? don't do that at the beginning.
6. are you sure that a cnn is the proper model for signal classification ?
7. how do you want to represent your dataset ? what should be the 3rd axes represent as an information ?
8. btw dropouts makes it also more difficult for the model to overfit. you use this so the model learns to generalize
9. i think the model is way to complex when the task is actually trivial. but i never did any signal classification
10. the use of sigmoid can lead to exploding gradients