  This paper investigates methods for quantifying similarity between audio
signals, specifically for the task of of cover song detection. We consider an
information-theoretic approach, where we compute pairwise measures of
predictability between time series. We compare discrete-valued approaches
operating on quantised audio features, to continuous-valued approaches. In the
discrete case, we propose a method for computing the normalised compression
distance, where we account for correlation between time series. In the
continuous case, we propose to compute information-based measures of similarity
as statistics of the prediction error between time series. We evaluate our
methods on two cover song identification tasks using a data set comprised of
300 Jazz standards and using the Million Song Dataset. For both datasets, we
observe that continuous-valued approaches outperform discrete-valued
approaches. We consider approaches to estimating the normalised compression
distance (NCD) based on string compression and prediction, where we observe
that our proposed normalised compression distance with alignment (NCDA)
improves average performance over NCD, for sequential compression algorithms.
Finally, we demonstrate that continuous-valued distances may be combined to
improve performance with respect to baseline approaches. Using a large-scale
filter-and-refine approach, we demonstrate state-of-the-art performance for
cover song identification using the Million Song Dataset.
