Multimedia scenarios have multimedia content and interactive events associated with computer programs. Interactive Scores (IS) is a formalism to represent such scenarios by temporal objects, temporal relations (TRs) and interactive events. IS describe TRs, but IS cannot represent TRs together with conditional branching. We propose a model for conditional branching timed IS in the Non-deterministic Timed Concurrent Constraint (ntcc) calculus. We ran a prototype of our model in Ntccrt (a real-time capable interpreter for ntcc) and the response time was acceptable for real-time interaction. An advantage of ntcc over Max MSP or Petri Nets is that conditions and global constraints are represented declaratively.
Another kind of systems capable of real-time interaction are systems (see @cite_6 ). Such systems track the performance of a real instrument and they may play multimedia associated to certain notes of the piece. However, to use these systems it is necessary to play a real instrument; whereas to use IS, the user only has to control some parameters of the piece, such as the start and end dates of the TOs. A model for multimedia interaction that does not require a real instrument uses Hidden Markov Models to model probabilistic installations @cite_41 . The system tracks human motion and it responds to human performance with chords and pitches depending on the knowledge of previous training. However, the system requires intensive training and it is not a tool for composition.
@cite_41 @cite_6
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The capacity for real-time synchronization and coordination is a common ability among trained musicians performing a music score that presents an interesting challenge for machine intelligence. Compared to speech recognition, which has influenced many music information retrieval systems, music's temporal dynamics and complexity pose challenging problems to common approximations regarding time modeling of data streams. In this paper, we propose a design for a real-time music-to-score alignment system. Given a live recording of a musician playing a music score, the system is capable of following the musician in real time within the score and decoding the tempo (or pace) of its performance. The proposed design features two coupled audio and tempo agents within a unique probabilistic inference framework that adaptively updates its parameters based on the real-time context. Online decoding is achieved through the collaboration of the coupled agents in a Hidden Hybrid Markov semi-Markov framework, where prediction feedback of one agent affects the behavior of the other. We perform evaluations for both real-time alignment and the proposed temporal model. An implementation of the presented system has been widely used in real concert situations worldwide and the readers are encouraged to access the actual system and experiment the results. This paper presents a probabilistic model of temporal structure in music which allows joint inference of tempo, meter and rhythmic pattern. The framework of the model naturally quantifies these three musical concepts in terms of hidden state-variables, allowing resolution of otherwise apparent ambiguities in musical structure. At the heart of the system is a probabilistic model of a hypothetical ‘bar-pointer’ which maps an input signal to one cycle of a latent, periodic rhythmical pattern. The system flexibly accommodates different input signals via two observation models: a Poisson points model for use with MIDI onset data and a Gaussian process model for use with raw audio signals. The discrete state-space permits exact computation of posterior probability distributions for the quantities of interest. Results are presented for both observation models, demonstrating the ability of the system to correctly detect changes in rhythmic pattern and meter, whilst tracking tempo. We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval. We present the first implementation of a new tool for prototyping digital musical instruments, which allows a user to literally grab a controller and turn it into a new, playable musical instrument almost instantaneously. The tool briefly observes a user interacting with a controller or sensors (without making any sound), and then it automatically generates a mapping from this observed input space to the control of an arbitrary sound synthesis program. The sound is then immediately manipulable using the controller, and this newly-created instrument thus invites the user to begin an embodied exploration of the newly-created relationships between human movement and sound. We hypothesize that this approach offers a useful alternative to both the creation of mappings by programming and to existing supervised learning approaches that create mappings from labeled training data. We have explored the potential value and trade-offs of this approach in two preliminary studies. In a workshop with disadvantaged young people who are unlikely to learn instrumental music, we observed advantages to the rapid adaptation afforded by this tool. In three interviews with computer musicians, we learned about how this “grab-and-play” interaction paradigm might fit into professional compositional practices.