Method for detecting abnormal events in a given environment, by analyzing audio signals recorded in said environment, the method comprising a step of modelling a normal ambiance by at least one model and a step of using the model or models, the method comprising at least the following steps: a model construction step (2.1) comprising at least the following steps: a) a step (3.1) of unsupervised initialization of Q groups consisting of a grouping by classes, or subspace of the normal ambiance, of the audio data representing the learning signals S A, Q being set and greater than or equal to 2, b) a step (3.2) of definition of a model of normality consisting of 1-class SVM classifiers, each classifier representing a group, each group of learning data defines a sub-class in order to obtain a model of normality consisting of several classifiers of 1-class SVM, each one being adapted to a group, or sub-set of data said to be normal derived from the learning signals representative of the ambiance, c) a step (3.3) of optimisation of the groups that uses the model obtained in step (3.2) so as to redistribute the data in the Q different groups, the aberrant data corresponding to values not belonging to the groups are rejected, d) repetition of the steps b and c until a stop criterion C 1, is checked and a model M is obtained, the step of use of the model(s) M obtained from the construction step comprising at least the following steps: e) the analysis (2.4) of an unknown audio signal S T obtained from the environment to be analyzed, the unknown audio signal is compared to the model M obtained from the model construction step, and assigns, for each 1-class SVM classifier, a score fq, and f) a comparison of all the scores fq obtained by the 1-class SVM classifiers using decision rules in order to determine (2.5) the presence or absence of an anomaly in the audio signal analyzed. a model construction step (2.1) comprising at least the following steps: a) a step (3.1) of unsupervised initialization of Q groups consisting of a grouping by classes, or subspace of the normal ambiance, of the audio data representing the learning signals S A, Q being set and greater than or equal to 2, b) a step (3.2) of definition of a model of normality consisting of 1-class SVM classifiers, each classifier representing a group, each group of learning data defines a sub-class in order to obtain a model of normality consisting of several classifiers of 1-class SVM, each one being adapted to a group, or sub-set of data said to be normal derived from the learning signals representative of the ambiance, c) a step (3.3) of optimisation of the groups that uses the model obtained in step (3.2) so as to redistribute the data in the Q different groups, the aberrant data corresponding to values not belonging to the groups are rejected, d) repetition of the steps b and c until a stop criterion C 1, is checked and a model M is obtained, a) a step (3.1) of unsupervised initialization of Q groups consisting of a grouping by classes, or subspace of the normal ambiance, of the audio data representing the learning signals S A, Q being set and greater than or equal to 2, b) a step (3.2) of definition of a model of normality consisting of 1-class SVM classifiers, each classifier representing a group, each group of learning data defines a sub-class in order to obtain a model of normality consisting of several classifiers of 1-class SVM, each one being adapted to a group, or sub-set of data said to be normal derived from the learning signals representative of the ambiance, c) a step (3.3) of optimisation of the groups that uses the model obtained in step (3.2) so as to redistribute the data in the Q different groups, the aberrant data corresponding to values not belonging to the groups are rejected, d) repetition of the steps b and c until a stop criterion C 1, is checked and a model M is obtained, the step of use of the model(s) M obtained from the construction step comprising at least the following steps: e) the analysis (2.4) of an unknown audio signal S T obtained from the environment to be analyzed, the unknown audio signal is compared to the model M obtained from the model construction step, and assigns, for each 1-class SVM classifier, a score fq, and f) a comparison of all the scores fq obtained by the 1-class SVM classifiers using decision rules in order to determine (2.5) the presence or absence of an anomaly in the audio signal analyzed. e) the analysis (2.4) of an unknown audio signal S T obtained from the environment to be analyzed, the unknown audio signal is compared to the model M obtained from the model construction step, and assigns, for each 1-class SVM classifier, a score fq, and f) a comparison of all the scores fq obtained by the 1-class SVM classifiers using decision rules in order to determine (2.5) the presence or absence of an anomaly in the audio signal analyzed.