A tutorial on deep signal processing - Measuring Distance Using Convolutional Neural Network
In signal processing, it is sometimes necessary to measure horizontal distance between some features of the signal, for example, the peaks. A good example of this could be interpreting an electrocardiogram (ECG), which relies on measuring distances for much of its interpretation. We will consider a toy example of the smooth signal with only two peaks in the picture below.
Image created by the author
The problem is simple enough and can be solved by finding peaks and then measuring a horizontal distance between them by subtracting their X coordinates. This can be done efficiently using available tools and libraries. Our goal is however to train a neural network to predict the distance between the peaks. Once we prove that a neural network can handle this task, we can reuse the same architecture in for more complex end-to-end learning tasks, when measuring the distance is only a means of learning more complex relationship. This stems from the idea of deep learning that we should try to have the neural network learn features rather have the engineers hand-code the features hoping these are the most relevant ones. If we can prove that a neural network can learn distance features, we can use it in more complex networks, where the end result will depend on many other factors other than the distances. Good examples of these task are interpreting ECG or astronomical data.
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