 In this video, we will see the DTFT of the impulse response of low pass FIR filter of length 2N plus 1. In time domain, we have obtained impulse response of FIR filter by multiplying impulse response of ideal low pass filter and a symmetric discrete time window function of length 2N plus 1. This is the magnitude spectrum of window function. This is the magnitude spectrum of ideal low pass filter. We can use the moving train analogy to visualize the convolution between H omega and V omega. Following is the animation when we change omega continuously from minus pi to plus pi. When omega comes in between minus omega c to plus omega c, where omega c is the cutoff of the low pass filter, then the major low will be contributing, which again diminishes when omega becomes greater than omega c. What we understood from this video? The effect on DTFT of a low pass filter when the impulse response is truncated, preserving its evenness. This concept is known as finite impulse response low pass filter design.