|Abstract||Timbre is one the fundamental elements for the identification of a musical instrument and is closely connected with its perceived quality and production type (blown, plucked, etc.). Thus, timbre is heavily responsible for each instrument's character and color and consequently responsible for its perceptual identification. An application that aims to the timbral transformation of one instrument into another, should address the issues of capturing the timbral characteristics of both source and target and converting one into another. This must be carried out in such a way, so that the listener, ideally, should not be able to distinguish a recording of the target instrument from the result of the transformation. In this thesis, we consider a method that is based on timbre modeling by means of the spectral envelope and using Gaussian mixture models (GMMs) extracts a function for instrument transformation. Our proposed framework is based on prior work and theory on voice conversion and incorporates a Line Spectral Frequencies (LSFs)-based representation of an all-pole model of the spectral envelope to perform transformation of the source instrument envelope into that of the target. We will be adapting principles from voice conversion, proposing several adjustments, modifications and additions in order to make it meaningful for instrument timbre transformation. The resulting framework which performance we present and evaluate, will be referred to as Instrument Transformation Framework (ITF).