In this paper we present a method aiming at analyzing the content an audio signal by using an artificial intelligence technique: Support Vector Machines (SVM). The objective is to detect the different events occurring in an unknown audio signal for information retrieval purposes. We present particularly the detection of violent events in a video.
There are two types of data mining, depending on whether the aim is to describe or rather to predict. In the specific case of audio data mining, on the one hand there is a descriptive method consisting of classifying a set of audio signals into the most similar groups of signals from a perception viewpoint. This is unsupervised classification. On the other hand, there is the predictive method consisting in designing a model from a learning database. In this way, any new audio signal could be automatically classified on the basis of the built model. This method is the supervised classification. The present paper deals with the supervised classification.
There are various supervised classification algorithms, such as decision trees, neurone networks, etc. However, we chose Support Vector Machine (SVM) which, according to the literature gives good results for real-world applications.
Firstly, we will describe the database or corpus. In a second section, we will present features used to describe the stimuli of the corpus. The third part of the paper will be devoted to brief theory on SVM algorithm. Finally, we will present the results of our study before drawing conclusions from this work.