1. A method for classifying a video, comprising the steps of: defining a set of classes for classifying an audio signal of a video; combining selected classes of the set as a subset of important classes, the subset of important classes is important for a specific highlighting task; combining the remaining classes of the set as a subset of other classes; training jointly the subset of important classes and the subset of other classes with training audio data to form a task specific classifier; classifying the audio signal using the task specific classifier as either important or other to identify highlights in the video corresponding to the specific highlighting task; representing the subset of important classes with a first Gaussian mixture model; and representing the subset of other classes with a second Gaussian mixture model, in which a number C of the subsets of classes is 2, and there are N train samples in a vector x of the training audio data, and each sample x i has an associated class label y i that takes on values 1 to C, and the task specific classifier has a form: f ⁡ ( x ; m ) = arg ⁢ ⁢ max y ⁢ p ⁡ ( x | y , m y , Θ y ) , where arg ⁢ ⁢ max y ⁢ p ⁡ ( x | y , m y , Θ y ) is a value of y for which p(x|y, m y , Θ y ) has a largest value, p stands for a condition probability, where the symbol | indicates a condition of the probability of the sample x given the class label y, m=[m 1 ,. .. , m c ] T is a number of mixture components for each Gaussian mixture model, and Θ represents parameters of each Gaussian mixture model.