Happy or Evil Laughter? Analysing a Database of Natural Audio Samples

We conducted a data collection on the basis of the Google AudioSet database by selecting a subset of the samples annotated with \textit{laughter}. The selection criterion was to be present a communicative act with clear connotation of being either positive (laughing with) or negative (being laughed at). On the basis of this annotated data, we performed two experiments: on the one hand, we manually extract and analyze phonetic features. On the other hand, we conduct several machine learning experiments by systematically combining several automatically extracted acoustic feature sets with machine learning algorithms. This shows that the best performing models can achieve and unweighted average recall of .7.