In recent years, source separation has been a central research topic in music signal processing, with applications in stereo-to-surround up-mixing, remixing tools for DJs or producers, instrument-wise equalizing, karaoke systems, and pre-processing in music analysis tasks. Musical sound sources, however, are often strongly correlated in time and frequency, and without additional knowledge about the sources a decomposition of a musical recording is often infeasible. To simplify this complex task, various methods have been proposed in recent years which exploit the availability of a musical score. The additional instrumentation and note information provided by the score guides the separation process, leading to significant improvements in terms of separation quality and robustness. A major challenge in utilizing this rich source of information is to bridge the gap between high-level musical events specified by the score and their corresponding acoustic realizations in an audio recording. In this article, we review recent developments in score-informed source separation and discuss various strategies for integrating the prior knowledge encoded by the score.

In the last years, various algorithms have been proposed for automatic classification and retrieval of motion capture data. Here, one main difficulty is due to the fact that similar types of motions may exhibit significant spatial as well as temporal variations. To cope with such variations, previous algorithms often rely on warping and alignment techniques that are computationally time and cost intensive. In this paper, we present a novel keyframe-based algorithm that significantly speeds up the retrieval process and drastically reduces memory requirements. In contrast to previous index-based strategies, our recursive algorithm can cope with temporal variations. In particular, the degree of admissible deformation tolerance between the queried keyframes can be controlled by an explicit stiffness parameter. While our algorithm works for general multimedia data, we concentrate on demonstrating the practicability of our concept by means of the motion retrieval scenario. Our experiments show that one can typically cut down the search space from several hours to a couple of minutes of motion capture data within a fraction of a second.

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