Mixed Excitation Linear Prediction Vocoder Algorithm

Recently, with the development of broadband communication, it seems that the band is not a serious problem any more. But in wireless communication field , band is always a kind of rare resource. Especially in military and secret communication, any improvement in speech coding may enhance the system's performance rapidly. Digital speech's store is also an important field that requires high quality speech coding algorithm because now all kinds of portable digital recorder is more and more popular. The demand is the power forcing speech coding to progress. Traditionally linear prediction(LPC) vocoders are very efficient, which can encode speech from 800 to 2400bps, but unfortunately, artifacts such as buzzes, thump, and tonal noise always exist in them.Mixed excitation linear prediction(MELP) vocoder is a kind of speech coding algorithm providing superior speech quality under very low rate even 1.2kbps, as well as its capability withstanding strong background noise. MELP vocoders base on LPC vocoders. Furthermore they add some new features to mimic the natural speech. MELP vocoders utilize mixed pulse and noise as the excitation to elimate the buzzes in traditional LPC vocoders, and add a jitter voicing state to overcome the tonal noise. Parameters' interpolation, adaptive spectrum enhancement and pulse dispersion also are adopted to improve the continuity. The synthetic speech of MELP vocoders sound much more natural and perceivable than the traditional vocoders'.Basing the American federal 2.4kbps MELP algorithm, the analysis platform was established for analyzing the and testing the performance of MELP codec. This article analyzes the capability of the MELP vocoders. Finally some advice are given to realize the vocoder in hardware or software.Chapter 2 introduces the theory of MELP vocodes, and expounds the features in detail. The essence and the function of these features is focused in this chapter. Chapter 3 introduces the basic algorithm of MELP vocoders, and some advanced skills such as muti-stage vector quantization and super resolution pitch detect algorithm. Chapter 4 utilizes the analysis platform toanalyze the capability of MELP vocoders and the features' performance. Finally aiming at the character of MELP algorithm, some advices about realizing it are given.