Understandable models Of music collections based on exhaustive feature generation with temporal statistics

Data mining in large collections of polyphonic music has recently received increasing interest by companies along with the advent of commercial online distribution of music. Important applications include the categorization of songs into genres and the recommendation of songs according to musical similarity and the customer's musical preferences. Modeling genre or timbre of polyphonic music is at the core of these tasks and has been recognized as a difficult problem. Many audio features have been proposed, but they do not provide easily understandable descriptions of music. They do not explain why a genre was chosen or in which way one song is similar to another. We present an approach that combines large scale feature generation with meta learning techniques to obtain meaningful features for musical similarity. We perform exhaustive feature generation based on temporal statistics and train regression models to summarize a subset of these features into a single descriptor of a particular notion of music. Using several such models we produce a concise semantic description of each song. Genre classification models based on these semantic features are shown to be better understandable and almost as accurate as traditional methods.