Abstract: Techniques for detecting abnormal audio events in a given environment, including learning the modeling of the environment to be surveilled during which a database is created by extraction of acoustic parameters associated with audio streams picked up over a fixed time period and an unsupervized automatic segmentation of said streams, followed by grouping the segments in classes and a statistical modeling of the segment classes, a usage phase including analysis of an audio stream, with the extraction of the acoustic parameters, automatic segmentation of said analyzed stream substantially 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 analyzed audio stream, resulting in a likelihood value which is compared to a threshold value to determine the presence or absence of audio anomalies in the analyzed audio stream.

Abstract: A method and system for detecting abnormal events in a given environment comprises a model construction step comprising: a) a step of unsupervised initialization of Q groups; b) a step of definition of a model of normality consisting of 1-class SVM classifiers; c) a step of optimum distribution of the audio signals in the Q different groups; d) repetition of the steps b and c until a stop criterion C1, is checked and a model M is obtained; and a step of use of the model(s) M obtained from the construction step comprising the analysis of an unknown audio signal ST assigning a score to a 1-class SVM classifier, and a comparison of all the scores fq obtained using decision rules in order to determine the presence or absence of an anomaly in the audio signal analyzed.

Abstract: Techniques for detecting abnormal audio events in a given environment, including learning the modeling of the environment to be surveilled 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 the segments in classes and a statistical modeling of the segment classes, a usage phase including analysis of an audio stream, with the extraction of the acoustic parameters, automatic segmentation of said analysed stream substantially 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 which is compared to a threshold value to determine the presence or absence of audio anomalies in the analysed audio stream.