Howtosignalprocessing
- How much should you oversample?
- What are the condition of oversampling?
- When should we use oversampling?
- Should you oversample or undersample?
How much should you oversample?
Choosing an oversampling rate 2x or more instructs the algorithm to upsample the incoming signal thereby temporarily raising the Nyquist frequency so there are fewer artifacts and reduced aliasing. Higher levels of oversampling results in less aliasing occurring in the audible range.
What are the condition of oversampling?
Oversampling unnecessarily increases the ADC output data rate and creates setup and hold-time issues, increases power consumption, increases ADC cost and also FPGA cost, as it has to capture high speed data.
When should we use oversampling?
When one class of data is the underrepresented minority class in the data sample, over sampling techniques maybe used to duplicate these results for a more balanced amount of positive results in training. Over sampling is used when the amount of data collected is insufficient.
Should you oversample or undersample?
Oversampling methods duplicate or create new synthetic examples in the minority class, whereas undersampling methods delete or merge examples in the majority class. Both types of resampling can be effective when used in isolation, although can be more effective when both types of methods are used together.
