Contents

- 1 What is the input of RNN?
- 2 What is frequency in Fourier transform?
- 3 How do you find the frequency of a Fourier transform?
- 4 Does frequency-domain operate on Fourier transformation?
- 5 What are the two types of Fourier series?
- 6 What is the output of a Fourier transform?
- 7 What is the importance of frequency domain?
- 8 Why do we use Fourier transformation?
- 9 Can a Fourier transform be used in a RNN?
- 10 How is the Fourier transform used in neural networks?
- 11 How are RNNs used in the frequency domain?
- 12 How are STFT frames input to a RNN?

## What is the input of RNN?

Therefore, a RNN has two inputs: the present and the recent past. This is important because the sequence of data contains crucial information about what is coming next, which is why a RNN can do things other algorithms can’t.

## What is frequency in Fourier transform?

The Fourier transform of a function of time is a complex-valued function of frequency, whose magnitude (absolute value) represents the amount of that frequency present in the original function, and whose argument is the phase offset of the basic sinusoid in that frequency.

## How do you find the frequency of a Fourier transform?

Let X = fft(x) . Both x and X have length N . Suppose X has two peaks at n0 and N-n0 . Then the sinusoid frequency is f0 = fs*n0/N Hertz.

## Does frequency-domain operate on Fourier transformation?

The so-called spectrum of frequency components is the frequency-domain depiction of the signal. However, as the name implies, the inverse Fourier transform converts the frequency-domain function back to the time function. Managing antenna signals or audio transmission will change the type of analysis used.

## What are the two types of Fourier series?

Fourier series is of two types- trigonometric series and exponential series.

## What is the output of a Fourier transform?

The Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components. The output of the transformation represents the image in the Fourier or frequency domain, while the input image is the spatial domain equivalent.

## What is the importance of frequency domain?

The frequency domain representation of a signal allows you to observe several characteristics of the signal that are either not easy to see, or not visible at all when you look at the signal in the time domain. For instance, frequency-domain analysis becomes useful when you are looking for cyclic behavior of a signal.

## Why do we use Fourier transformation?

The Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components. The Fourier Transform is used in a wide range of applications, such as image analysis, image filtering, image reconstruction and image compression.

## Can a Fourier transform be used in a RNN?

In particular, as recurrent neural networks (RNNs) become the go-to choice analyzing temporal sequences, it becomes logical to incorporate Fourier transforms directly into the RNN framework. Doing so not only expands the analysis capabilities of RNNs, but also imparts several computational advantages.

## How is the Fourier transform used in neural networks?

Our Fourier RNN is able to analyze and predict temporal sequences competitive with current time-based methods at a fraction of the computational cost. The Fourier transform has been noted in the past for improving the computational efficiency of convolutional neural networks (CNNs)

## How are RNNs used in the frequency domain?

We can move RNN processing into the frequency domain and define our Fourier RNN (fRNN) by applying the STFT to the input signal x. Assuming that a projection of the final hidden vector is also signal of the temporal domain, we can apply the iSTFT to recover the output y.

## How are STFT frames input to a RNN?

Typically each STFT frame is a single time step input to a RNN. You could input the frequency-domain values in fixed order (e.g. low to high frequency) one at a time into a RNN too, but that would be unusual and would make most learning tasks harder. Thanks for contributing an answer to Artificial Intelligence Stack Exchange!