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This paper describes a MIR-based system for live musical performance between a human and a robot. This project involves combining human computer interface with musical robotics using query/retrieval architecture to create musical rhythmic phrases towards improvisation.

This paper describes a system enabling a human to perform music with a robot in real-time, in the context of North Indian classical music. We modify a traditional acoustic sitar into a hyperinstrument in order to capture performance gestures for musical analysis. A custom built four-armed robotic Indian drummer was built using a microchip, solenoids, aluminum and folk frame drums. Algorithms written towards “intelligent” machine musicianship are described. The final goal of this research is to have a robotic drummer accompany a professional human sitar player live in performance.

This report describes the design and realization of Mimi, a multi-modal interactive musical improvisation system that explores the potential and powerful impact of visual feedback in performer-machine interaction. Mimi is a performer-centric tool designed for use in performance and teaching. Its key and novel component is its visual interface, designed to provide the performer with instantaneous and continuous information on the state of the system. For human improvisation, in which context and planning are paramount, the relevant state of the system extends to the near future and recent past. The Mimi system, designed and implemented using the SAI framework, successfully integrates symbolic computations and real-time synchronization in a multi-modal interactive setting. Mimi’s visual interface allows for a peculiar blend of raw reflex typically associated with improvisation, and preparation and timing more closely affiliated with score-based reading. Mimi is not only an effective improvisation partner, it has also proven itself to be an invaluable platform through which to interrogate the mental models necessary for successful improvisation. Keywords: Performer-machine interaction, visualization design, machine improvisation Figure 1: Aural and visual performer-machine interaction: Dennis Thurmond and Mimi.

The paper describes an interactive musical system that utilizes a genetic algorithm in an effort to create inspiring collaborations between human musicians and an improvisatory robotic xylophone player. The robot is designed to respond to human input in an acoustic and visual manner, evolving a human-generated phrase population based on a similarity driven fitness function in real- time. The robot listens to MIDI and audio input from human players and generates melodic responses that are informed by the analyzed input as well as by internalized knowledge of contextually relevant material. The paper describes the motivation for the project, the hardware and software design, two performances that were conducted with the system, and a number of directions for future work. 1. INTRODUCTION AND RELATED

Music can be used both to direct and enhance the impact a story can have on its listeners. This work makes use of two creative systems to provide emotionally-targeted musical accompaniment for stories. One system assigns emotional labels to text, and the other generates original musical compositions with targeted emotional content. We use these two programs to generate music to accompany audio readings of fairy tales. Results show that music with targeted emotional content makes the stories significantly more enjoyable to listen to and increases listener perception of emotion in the text.

In this paper we describe the digital emulation of a optical photosonic instrument. First we briefly describe the optical instrument which is the basis of this emulation. Then we give a musical description of the instrument implementation and its musical use and we conclude with the "duo" possibility of such an emulation.

Current research programmes in computer music may draw from developments in agent technology; music may provide an excellent test case for agent research. This paper describes the challenge of building agents for concert performance which allow close and rewarding interaction with human musicians. This is easier said than done; the fantastic abilities of human musicians in fluidity of action and cultural reference makes for a difficult mandate. The problem can be cast as that of building an autonomous agent for the (unforgiving) realtime musical environment. Live music is a challenging domain to model, with high dimensionality of descriptions and fast learning, responses and effective anticipation required. A novel symbolic interactive music system called Improvagent is presented as a framework for the testing of reinforcement learning over dynamic state-action case libraries, in a context of MIDI piano improvisation. Reinforcement signals are investigated based on the quality of musical prediction, and on the degree of influence in interaction. The former is found to be less effective than baseline methods of assumed stationarity and of simple nearest neighbour case selection. The latter holds more promise; an agent may be able to assess the value of an action in response to an observed state with respect to the potential for stability, or the promotion of change in future states, enabling controlled musical interaction. 1.

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We propose an online generative algorithm to enhance musical expression via intelligent improvisation accompaniment. Our framework called the ImprovGenerator, takes a live stream of percussion patterns and generates an improvised accompaniment track in real-time to stimulate new expressions in the improvisation. We use a mixture model to generate an accompaniment pattern, that takes into account both the hierarchical temporal structure of the live input patterns and the current musical context of the performance. The hierarchical structure is represented as a stochastic context-free grammar, which is used to generate accompaniment patterns based on the history of temporal patterns. We use a transition probability model to augment the grammar generated pattern to take into account the current context of the performance. In our experiments we show how basic beat patterns performed by a percussionist on a cajon can be used to automatically generate on-the-fly improvisation accompaniment for live performance.