Concerto for violin and Markov model

in the OPening moments of Jean Sibel-ius' Violin Concerto, the young soloist plays delicately, almost languidly. The orchestra responds in kind, muting the repeated string motif to a whisper. As the piece progresses, soloist and orchestra alternatively perform the main motifs in increasing measures of power and virtuosity, which inexorably lead toward the movement's stirring resolution. The soloist looks relieved as she crosses the stage to shake the conduc-tor's hand. This violinist, like most others in music education, can benefit enormously from interacting with large ensembles in honing her performing skills. However, the demand far exceeds the number and capabilities of existing orchestras, ensuring most of these students won't have access to this experience. Our soloist is no exception. The previous paragraph describes her interaction with Chris Raphael's Music Plus One system: A machine learning-driven alternative to working with orchestras that retains much of the ex-pressivity and interactivity that makes concerto performance such a rewarding and educational experience. The following paper details the approach, for videos see Automatic music accompaniment has been actively researched since the 1980s, starting with the work of such pioneers as Barry Vercoe and Roger Dan-nenberg. The problem can be broken into three subparts: 1 tracking the playing of a human soloist, matching it to a known musical score, and synthesizing an appropriate accompaniment to that solo part in real time. Solutions usually involve ingenious pattern-matching mechanisms for dealing with expressive , incorrectly played or missing notes in the soloist performance, while using the output of the pattern match to drive the scheduling of accompaniment events. However, as Raphael notes, it is impossible to accomplish score following by reaction alone. The system must incorporate a predictive component that attempts to align upcoming notes of the accompaniment with imminent attacks of the human player. Failing to solve this problem can result in potentially disastrous consequences for the performance. The proposed approach starts by using a hidden Markov model-based score follower tasked with estimating the start time of the notes played by the soloist and matching them to a position in the score. The model considers the sequence of frame-wise signal features, characterizing transient and pitch information on the audio input, as its output , and the state graph for the Markov chain as a sequence of note sub-graphs modeling the soloist's performance. In a process akin to echo cancellation, the contribution of the accompanist to the audio …