  Music summarization allows for higher efficiency in processing, storage, and
sharing of datasets. Machine-oriented approaches, being agnostic to human
consumption, optimize these aspects even further. Such summaries have already
been successfully validated in some MIR tasks. We now generalize previous
conclusions by evaluating the impact of generic summarization of music from a
probabilistic perspective. We estimate Gaussian distributions for original and
summarized songs and compute their relative entropy, in order to measure
information loss incurred by summarization. Our results suggest that relative
entropy is a good predictor of summarization performance in the context of
tasks relying on a bag-of-features model. Based on this observation, we further
propose a straightforward yet expressive summarizer, which minimizes relative
entropy with respect to the original song, that objectively outperforms
previous methods and is better suited to avoid potential copyright issues.
