Take a look at [0] to get a quick visual demonstration of how that works. As others have said, lossy compression often throws away high frequency data (for soun

Take a look at [0] to get a quick visual demonstration of how that works. As others have said, lossy compression often throws away high frequency data (for sound, it corresponds to high pitches that you can barely hear. For images, high frequency corresponds to fast changing parts of a picture, so think crisp edges, fine detail (e.g. hair or stubble), and noise/film grain). So a basic lossy compression algorithm might use a FT and then only store the lower frequencies. When you do the inverse transform without the high frequency data, you lose details. Note that the spectrograms in [0] use what's called a short time fourier transform[1]. Basically, split your song into windows (e.g. 1 second each), and do a fourier transform on each window. This lets you get a picture of the spectrum over time. [0] https://sound.stackexchange.com/questions/38709/320kbps-mp3-why-is-there-a-clear-line-near-16khz-on-spectrogram https://sound.stackexchange.com/questions/38709/320kbps-mp3-... [1] https://en.wikipedia.org/wiki/Short-time_Fourier_transform https://en.wikipedia.org/wiki/Short-time_Fourier_transform