What is the importance of linear prediction in signal processing?

Answer 1

For one example, linear prediction is used for predicting the next "sample" of human voice in conversation, at the sending side of the conversation. The actual next sample is subtracted from the predicted sample and this difference is called the error. The sending side encodes and transmits only the error signal because the receiving side uses the same prediction algorithm and can reconstruct the error free signal equals the predicted signal plus the received error signal.

There's an advantage only if the error is small enough to be transmitted with fewer bits.

Answer 2

Linear prediction in signal processing is important because it allows for the estimation of future values of a signal based on its past values. This is especially useful in applications such as speech and audio coding, where the accurate prediction of future samples can lead to efficient compression algorithms. Linear prediction also finds applications in noise reduction and speech enhancement techniques.