Inverse Discrete Fourier Transform Calculator

Inverse Discrete Fourier Transform

The Discrete Fourier Transform (DFT) yields frequency-domain representations of signals; the Inverse Discrete Fourier Transform (IDFT) is a mathematical approach that returns these representations to their original time-domain form.
Engineers can now evaluate and work with signals in the time domain after processing them in the frequency domain, thus reversing the DFT process.

The basic goal of the IDFT is to retrieve time-domain signals from their frequency-domain representations so that time-domain signal properties can be analyzed and understood by engineers.
Engineers can gain insights into signal behavior, amplitude, phase, and timing by executing the IDFT; these insights are crucial for developing and debugging electronic circuits and systems.

Understanding Inverse Discrete Fourier Transform:


Linearity: The Inverse Discrete Fourier Transform (IDFT) is indeed a linear transformation, which implies that it conserves the linearity of the input signal.

Time reversal

The IDFT does indeed reverse the time-domain signal, resulting in an output signal that is the reverse of the input signal.

Frequency reversal: The IDFT also reverses the frequency-domain signal, meaning that the output signal is the reverse of the input signal in the frequency domain.

Real-valued: The IDFT produces a real-valued output signal if the input signal is real-valued.


Fast Fourier Transform (FFT)

The IDFT computation can be facilitated by employing the Fast Fourier Transform (FFT) algorithm, renowned for its efficiency in computing the Discrete Fourier Transform (DFT).

Inverse Fast Fourier Transform (IFFT): The IDFT can be computed using the Inverse Fast Fourier Transform (IFFT) algorithm, known for its efficiency in computing the IDFT.


Digital signal processing: The implementation of the IDFT can utilize digital signal processing methods like digital filters and modulation techniques.

Analog signal processing

The Inverse Discrete Fourier Transform (IDFT) can also be realized through analog signal processing methodologies like analog filtering and modulation techniques.


  • Signal Processing
  • Audio and Video Processing
  • Digital Communications
  • Spectral Analysis

While IDFT carries out the opposite transformation, converting signals from the frequency domain back to the time domain, DFT makes it easier to convert signals from the time domain to the frequency domain without any loss. In a variety of applications, these transformations are essential for signal analysis and manipulation.


The Inverse Discrete Fourier Transform (IDFT) serves as a valuable asset in converting frequency-domain signals back into their original time-domain counterparts. Its utility spans across various domains, including signal processing, image processing, and audio processing. Proficiency in comprehending the IDFT proves indispensable for individuals engaged in tasks involving signals and systems.

Note : Don’t end with comma ( , )

Enter Series Values
Ex : 11,22,3,4..(Upto 10 values)


\[x(n)=\frac{1}{N} āˆ‘_{k=0}^{Nāˆ’1} X(k)ā‹…e^{i2Ļ€\frac{kn}{N}}\]


  • x(n) ā€“ represents the time-domain signal
  • X(k) ā€“ represents the frequency-domain coefficients
  • N ā€“ is the total number of samples in the signal
  • i ā€“ is the imaginary unit

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