Problems in audio compression form just a subset of the general problem of signal compression, and general techniques can well be applied to solve them. However, it is possible to benefit greatly from being aware of the very particular way in which the human brain perceives and interprets sound, being able to optimize compression techniques to keep only information that is relevant to human perception. In this presentation, I focus on speech compression, and more particularly on an implementation using a Linear Predicting Model (LPM). The LPM provides a very efficient way of reconstructing a signal from a very small set of compressed data (up to 95% of data can be neglected), generating a sythesized speech that keeps the original phonemes and the quality of the voice of the speaker, who can be recognized easily. This technique has been used in telephony applications.
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