Training sequence models such as transformers with symbolic music requires a representation of music as sequences of atomic elements called tokens. State-of-the-art music tokenizations encode pitch values explicitly, which complicates the ability of a machine learning model to generalize musical knowledge at different keys. We propose tracks for a tokenization encoding pitch intervals rather than pitch values, resulting in transposition invariant representations. The musical expressiveness of this new tokenization is evaluated through two MIR classification tasks: composer classification and end of phrase detection. We release publicly the code produced in this research.