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A compact, general and accurate model of the timbral characteristics of musical instruments can be used as a source of a priori knowledge for music content analysis applications such as transcription and instrument classification, as well as for source separation. We develop a timbre model based on the spectral envelope that meets these requirements and relies on additive analysis, Principal Component Analysis and database training. We put special emphasis on the issue of frequency misalignment when training an instrument model with notes of different pitches, and show that a spectral representation involving fre- quency interpolation results in an improved model. Finally, we show the performance of the developed model when applied to musical instrument classification.