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This paper presents a survey of recent work in computerassisted musical instrumental tutoring and outlines several questions to consider when developing future projects. In particular, we suggest that the area in greatest need of computer assistance is enhancing daily practice: both motivating students to practice through games and multimedia, and providing an objective analysis of the students' performance. Many existing projects attempt to replace human teachers by providing lessons during daily practice; in most cases, these "daily lessons" are not necessary.

Tempo estimation is a fundamental problem in music information retrieval. It also forms the basis of other types of rhythmic analysis such as beat tracking and pattern detection. There is a large body of work in tempo estimation using a variety of different approaches that differ in their accuracy as well as their complexity. Fundamentally they take advantage of two properties of musical rhythm: 1) the music signal tends to be self-similar at periodicities related to the underlying rhythmic structure, 2) rhythmic events tend to be spaced regularly in time. We propose an algorithm for tempo estimation that is based on these two properties. We have tried to reduce the number of steps, parameters and modeling assumptions while retaining good performance and causality. The proposed approach outperforms a large number of existing tempo estimation methods and has similar performance to the best-performing ones. We believe that we have conducted the most comprehensive evaluation to date of tempo induction algorithms in terms of number of datasets and tracks.

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There is considerable interest in music-based games and apps. However, in existing games, music generally serves as an accompaniment or as a reward for progress. We set out to design a game where paying attention to the music would be essential to making deductions and solving the puzzle. The result is the CrossSong Puzzle, a novel type of music-based logic puzzle that integrates musical and logical reasoning. The game presents a player with a grid of tiles, each representing a mash-up of excerpts from two different songs. The goal is to rearrange the tiles so that each row and column plays a continuous musical excerpt. To create puzzles, we implemented an algorithm to automatically identify a set of song fragments to fill a grid such that each tile contains an acceptable mash-up. We present several optimisations to speed up the search for high-quality grids. We also discuss the iterative design of the game's interface and present the results of a user evaluation of the final design. Finally, we present some insights learned from the experience which we believe are important to developing music-based puzzle games that are entertaining, feasible and that challenge one's ability to think about music.

<p>The purpose of this study was to establish the extent to which flow can be predicted by the cognitive potential of the incumbent and the complexity of the work he/she performs. The sample consisted of 161 participants from a telecommunications company. The Flow Experience Survey (FES) was adapted and administered to the respondents. The Initial Recruitment Interview Schedule (IRIS) was used to measure capability and the Matrix of Working Relationships was used to measure job complexity. The FES and IRIS scales were subjected to factor analysis. An item analysis was also performed on the FES to determine the reliability of the instrument. There was a statistically significant relationship between capability and job complexity, but these variables did not relate to the flow experience. The implications of these findings are discussed.</p><p><strong>Opsomming</strong></p><p>Die doel van die studie was om te bepaal in watter mate psigiese &ldquo;vloei�? voorspel kan word deur die kognitiewe potensiaal van die posbekle&euml;r en die kompleksiteit van die werk wat hy/sy verrig. Die steekproef het uit 161 deelnemers van &lsquo;n telekommunikasie organisasie bestaan. Die "Flow Experience Survey" (FES) is aangepas en daarna toegepas op die deelnemers. Die "Initial Recruitment Interview Schedule�? (IRIS) is gebruik om kognitiewe potensiaal te meet, terwyl die "Matrix of Working Relationships" gebruik is om die kompleksiteit van werk te bepaal. Sowel die FES as IRIS skale is onderwerp aan &rsquo;n faktorontleding. Verder is &rsquo;n itemontleding uitgevoer op die FES om die betroubaarheid daarvan te bepaal. &rsquo;n Statisties beduidende verband tussen kognitiewe potensiaal en die kompleksiteit van werk, is gevind, alhoewel hierdie veranderlikes nie met "vloei" verband gehou het nie. Die implikasies van die studie word bespreek.</p>

This work improves the realism of synthesis and performance of string quartet music by generating audio through physical modelling of the violins, viola, and cello. To perform music with the physical models, virtual musicians interpret the musical score and generate actions which control the physical models. The resulting audio and haptic signals are examined with support vector machines, which adjust the bowing parameters in order to establish and maintain a desirable timbre. This intelligent feedback control is trained with human input, but after the initial training is completed, the virtual musicians perform autonomously. The system can synthesize and control different instruments of the same type (e.g., multiple distinct violins) and has been tested on two distinct string quartets (total of 8 violins, 2 violas, 2 cellos). In addition to audio, the system creates a video animation of the instruments performing the sheet music.

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