This paper deals with several of the possible uses of Zipf and inverse Zipf laws in the field of audio signal analysis. We show that these laws are powerful analysis tools allowing the extraction of information not available by standard methods. The adaptation of Zipf and inverse Zipf laws to audio signals requires a coding of these signals into text-like data, considered as sequences of patterns. Because these codings are of first importance since they have to bring to the fore relevant information within signals, three types of codings have been developed, depending on the representation of the audio signal it is based on: temporal, frequential and time-scale representations. Once audio signal has been coded, features linked to Zipf and inverse Zipf approaches are computed. Finally, the classification step aims at the identification of signals. Four classification methods have been considered as well as a fusion method that combines these classifiers. In order to evaluate our method, we apply it on medical acoustical signals. They occur when swallowing and contain xiphoidal sounds. The problem is to extract and characterize xiphoidal sounds according to the gastro-oesophageal reflux pathological state. The aim is to help medical doctors to characterize and diagnose this pathology, and to give, in the end, a decision help tool as efficient as possible.