The research presented in this dissertation focuses on expressivity-aware tempo transformations of monophonic audio recordings of saxophone jazz performances. It is a contribution to content-based audio processing, a field of technology that has recently emerged as an answer to the increased need to deal intelligently with the evergrowing amount of digital multimedia information available nowadays. We have investigated the problem of how a musical performance played at a particular tempo can be rendered automatically at another tempo, while preserving naturally sounding expressivity. This problem cannot be reduced to just applying a uniform transformation to all notes of the melody, since it often degrades the musical quality of the performance. We present a case-based reasoning system for expressivity aware tempo transformations. A validation of the system showed superior results compared to uniform transformation. Furthermore, contributions have been made to expressive performance analysis, CBR, melody retrieval, and evaluation methodologies of expressive models.