Heterogeneous mixture models using sparse representation features for applause and laugh detection
Ziqiang Shi, Jiqing Han, Tieran Zheng · 2011
A novel and robust approach for applause and laugh detection is proposed based on sparse representation features and heterogeneous mixture models (hetMM). The projections of the noise robust sparse representations for audio signals computed by L1- minimization are used as feature. We consider the classifiers based on heterogeneous mixture models (hetMM) which combine multiple different kinds of distributions, since in practice the data may come from multiple sources and it is often unclear what the most suitable distribution is. Experimental results show that method with hetMM has better results than using a single distribution type and gives comparable performances with Support Vector Machines (SVMs).