How does FFT relate to machine learning?

How does FFT relate to machine learning?

FFT, or Fast Fourier Transform, is a mathematical algorithm that transforms signals from the time domain to the frequency domain. In machine learning, it can be used for feature extraction, especially in signal processing tasks.

Can you give an example of how FFT is used in a machine learning project?

Sure! In audio classification tasks, FFT can be applied to convert audio signals into frequency components, which can then be used as features for training models to classify different sounds.

What are the benefits of using FFT in machine learning?

Using FFT can significantly reduce the computational complexity of processing signals, allowing for faster training and inference times in machine learning models.

Are there any limitations to using FFT?

Yes, FFT assumes that the signal is periodic and can introduce artifacts if the signal is not properly windowed. Additionally, it may not capture time-varying features well.