## Abstracts
### Off-line Score Alignment for Realistic Music Practice  
[PERSON], [PERSON], [PERSON], and [PERSON]  
In a common music practice scenario, a player works with a musical score, but may jump arbitrarily from one passage to another in order to drill on difficult technical challenges or pursue some other agenda requiring non-linear movement through the score. In this work, we treat the associated score alignment problem in which we seek to align a known symbolic score to the audio of the musician's practice session, identifying all \"do-overs\" and jumps. The result of this effort facilitates a quantitative view of a practice session, allowing feedback on coverage, tempo, tuning, rhythm, and other aspects of practice. If computationally feasible, we would prefer a globally optimal dynamic programming search strategy; however, we find such schemes only barely computationally feasible in the cases we investigate. Therefore, we develop a computationally efficient off-line algorithm suitable for practical application. We present examples, analyzing unsupervised and unscripted practice sessions on clarinet, piano, and viola, providing numerical evaluation of our score-alignment results on hand-labeled ground-truth audio data, as well as more subjective and easy-to-interpret visualizations of the results. S6.3. Piano Score--Following by Tracking Note Evolution  
[PERSON] and [PERSON]  
Score following matches musical performance audio with its symbolic score in an on-line fashion. Its applications are meaningful in music practice, performance, education, and composition. This paper focuses on following piano music--one of the most challenging cases. Motivated by the time-changing features of a piano note during its lifetime, we propose a new method that models the evolution of a note in spectral space, aiming to provide an adaptive, hence better, data model. This new method is based on a switching Kalman filter in which a hidden layer of continuous variables tracks the energy of the various note harmonics. The result of this method could potentially benefit applications in de-soloing, sound synthesis, and virtual scores. This paper also proposes a straightforward evaluation method. We conducted a preliminary experiment on a small dataset of 13 min of music, consisting of 15 excerpts of real piano recordings from eight pieces. The results show the promise of this new method. S6.4. Adaptive Score--Following System by Integrating Gaze Information  
[PERSON], [PERSON], and [PERSON]  
In actual piano practice, people of different skill levels exhibit different behaviors, for instance, leaping forward or to an upper staff, miskeying, repeating, and so on. However, many of the conventional score following systems hardly adapt to such accidental behaviors depending on individual skill level, because conventional systems usually learn the frequent or general behaviors. We develop a score-following system that can adapt a user's individuality by combining keying information with gaze, because it is well-known that the gaze is a highly reliable means of expressing a performer's thinking. Since it is difficult to collect a large amount of piano performance data reflecting individuality, we employ the framework of the Bayesian inference to adapt individuality. That is, to estimate the user's current position in piano performance, keying and gaze information are integrated into a single Bayesian inference by a Gaussian mixture model (GMM). Here, we assume both the keying and gaze information conform to normal distributions. Experimental results show that, taking into account the gaze information, our score-following system can properly cope with repetition and leaping to an upper row of a staff, in particular. S6.5. Alternative Measures: A Musicologist Workbench for Popular Music  
[PERSON] and [PERSON]  
The objective of this project is to create a digital \"workbench\" for quantitative analysis of popular music. The workbench is a collection of tools and data that allow for efficient and effective analysis of popular music.