Ordinal symbolic dynamics has developed into a valuable method to describe complex systems. Recently, using the concept of transcripts, the coupling behaviour of systems was assessed, combining the properties of the symmetric group with information theoretic ideas. In this contribution, methods from the field of ordinal symbolic dynamics are applied to the characterisation of audio data. Coupling complexity between frequency bands of solo violin music, as a fingerprint of the instrument, is used for classification purposes within a support vector machine scheme. Our results suggest that coupling complexity is able to capture essential characteristics, sufficient to distinguish among different violins.