Basic upsampling of a signal

1. What is upsampling in signal?
2. How do we do upsampling?
3. Why do we upsample a signal?
4. Is it possible to upsample the signal?

What is upsampling in signal?

Upsampling is the process of inserting zero-valued samples between original samples to increase the sampling rate. (This is sometimes called “zero-stuffing”.) This kind of upsampling adds undesired spectral images to the original signal, which are centered on multiples of the original sampling rate.

How do we do upsampling?

You can upsample a dataset by simply copying records from minority classes. You can do so via the resample() method from the sklearn. utils module, as shown in the following script. You can see that in this case, the first argument we pass the resample() method is our minority class, i.e. our spam dataset.

Why do we upsample a signal?

The purpose of upsampling is to add samples to a signal, whilst maintaining its length with respect to time. Consider again a time signal of 10 seconds length with a sample rate of 1024Hz or samples per second that will have 10 x 1024 or 10240 samples.

Is it possible to upsample the signal?

Upsampling is the process of inserting zero-valued samples between the original samples of a signal to increase the sampling rate. One way to accomplish upsampling by an integer ratio of 1:D is to interpose D-1 zero samples between each pair of the input samples of the signal.