1. A method for detecting abnormal audio events in a given environment, the method comprising: an automated step for learning a modeling of said environment during which a database is created by extraction of acoustic parameters associated with audio streams picked up over a fixed time period and an unsupervised automatic segmentation of said streams, followed by grouping segments into classes and a statistical modeling of the segment classes; and a usage phase which comprises analysis of an audio stream, with the extraction of the acoustic parameters, automatic segmentation of an analysed stream identical to that used during the learning phase and determining a likelihood of each statistical model contained in the database for each of the segments of the analysed audio stream, resulting in a likelihood value λ which corresponds to a most probable model, the maximum of the likelihoods, which is compared to a threshold value in order to initiate or not a signal indicating a presence or absence of audio anomalies in the analysed audio stream, wherein the learning step involves using an algorithm which positions centroids uniformly distributed in a space of the parameters by executing the following steps: 1) calculation of a mean vector from all the learning data; 2) search for the segment most distant from the mean vector and initialization of the first centroid as being the mean vector of the segment found (nb_class=1); and 3) iterations for incrementing a number of classes (nb_class=nb_class+1): the segment which maximizes a cumulative distance to the centroids identified in the preceding iteration is sought, wherein a stop criterion is either when a predefined number of classes is reached, or when the distance between the segment found and the centroids identified in the preceding iteration is below a threshold.