Inner structure computation for audio signal analysis

We present in this paper an audio signal classification method based on Zipf and inverse Zipf laws. These laws are powerful analysis tools allowing the extraction of information not available by the way of standard methods. The adaptation of Zipf and Inverse Zipf laws to audio signals requires a coding of these signals into literary texts, considered as sequences of patterns. Because these codings are of first importance since they have to bring to the fore relevant information in 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 signals have been coded, features linked to Zipf and inverse Zipf laws are computed. Finally, the classification step aims at the identification of signals. Four classification methods have been considered as well as a fusion method used to combine these classifiers. In order to evaluate our method, we have analysed medical signals corresponding to swallowing signals containing xiphoidal sounds. The problem is to characterize them according to the gastro-oesophageal reflux pathological state.