A non-supervised method of thematic analysis and structuring of an audio stream, said audio stream comprising a set of documents each consisting of several segments, said audio stream comprising one or more patterns or themes repeated over time, characterized in that it comprises at least the following steps: • Extracting the acoustic parameters of the audio stream to be analyzed, E1 • A step of low-level segmentation of said audio stream so as to obtain a set of segments S, E2, a segment corresponding to a concatenation of consecutive frames, • A step of fuzzy classification of said segments by using a dictionary of M predefined audio words, so as to characterize each segment S with a set of Q audio words and of Q degrees of membership wfk of a segment in a class, E4, an audio word being the representative of a class, a class being defined as a set of a similar segments, • Generating a cooccurrence matrix for each document of said audio stream, an element n(wi, dj) of the cooccurrence matrix corresponds to a combination of the degrees of membership of all the words of the document dj which is close to the class i in the sense of the fuzzy classification, the size M*N of the matrix corresponds to the number M of audio words and N to the number of documents, E5a • For a finite segmentation set, document size ti, start of the analysis of the audio stream, and on the basis of the associated cooccurrence matrix, determining, by adaptation of predefined statistical models, the likelihoods li associated with each pair (document size ti, start of analysis), E5c, the document size belonging to a predefined interval [tmin, tmax], • Seeking the optimal segmentation in terms of document size, in the sense of the maximum likelihood for each triplet (size of the document, start of analysis, likelihood) by application of an algorithm of dynamic programming type, E6, under the constraint of selecting documents without temporal overlap. • Extracting the acoustic parameters of the audio stream to be analyzed, E1 • A step of low-level segmentation of said audio stream so as to obtain a set of segments S, E2, a segment corresponding to a concatenation of consecutive frames, • A step of fuzzy classification of said segments by using a dictionary of M predefined audio words, so as to characterize each segment S with a set of Q audio words and of Q degrees of membership wfk of a segment in a class, E4, an audio word being the representative of a class, a class being defined as a set of a similar segments, • Generating a cooccurrence matrix for each document of said audio stream, an element n(wi, dj) of the cooccurrence matrix corresponds to a combination of the degrees of membership of all the words of the document dj which is close to the class i in the sense of the fuzzy classification, the size M*N of the matrix corresponds to the number M of audio words and N to the number of documents, E5a • For a finite segmentation set, document size ti, start of the analysis of the audio stream, and on the basis of the associated cooccurrence matrix, determining, by adaptation of predefined statistical models, the likelihoods li associated with each pair (document size ti, start of analysis), E5c, the document size belonging to a predefined interval [tmin, tmax], • Seeking the optimal segmentation in terms of document size, in the sense of the maximum likelihood for each triplet (size of the document, start of analysis, likelihood) by application of an algorithm of dynamic programming type, E6, under the constraint of selecting documents without temporal overlap.