The paper focuses on the experimentation of models and methods for expressive communication aiming at enhancing the interaction process between humans and mobile robots. These directions are explored by presenting the performance environments developed for the multimedia performance “L’Ala dei Sensi ” (literally, “The Wing of the Senses”), held in Ferrara (Italy) on November 1999. “L’Ala dei Sensi” consists of a percourse on the theme of human perception from different perspectives. In a few words, it aims at explaining scientific principles (on human perception mechanisms) by means of the language of art (dance, music, visual arts). Each episode focuses on a specific scientific issue which is explained and demonstrated in a performance. In this paper we describe and discuss the episodes involving interactive dance/music performances that also made use of a small mobile on-wheel robotic platform Pioneer 2 from Stanford Research Institute. These episodes consist of short dance performances in which the dancer himself is involved in a dialogue with the robot and with visual and musical clones on large videoprojection screens. Some research issues arising from the experiments in “L’Ala dei Sensi ” as well as in utilising mobile robots in other theatre and museum applications (Camurri and Coglio, 1998; Camurri et al., 1999) are discussed: (i) Expressiveness in human-robot interaction. How can we convey expressive content in a context (an artistic performance, a museum exhibit) involving a mobile robot? Can expressiveness arise from the movement of the

The performance of music usually involves a great deal of interpretation by the musician. In classical music, the final ritardando is a good example of the expressive aspect of music performance. Even though expressive timing data is expected to have a strong component that is determined by the piece itself, in this paper we investigate to what degree individual performance style has an effect on the timing of final ritardandi. The particular approach taken here uses Friberg and Sundberg’s kinematic rubato model in order to characterize performed ritardandi. Using a machinelearning classifier, we carry out a pianist identification task to assess the suitability of the data for characterizing the individual playing style of pianists. The results indicate that in spite of an extremely reduced data representation, when cancelling the piece-specific aspects, pianists can often be identified with accuracy above baseline. This fact suggests the existence of a performer-specific style of playing ritardandi. 1.

Background in Music Performance. The first empirical studies on music performance date back to the beginning of the 20th century, mainly focusing on timing in performance. In the last decades, performance studies have earned recognition as a discipline in its own right. Background in Computing. Machine learning and data mining techniques have been widely applied to expressive music performance, focusing on finding general principles underlying expressive 'deviations ' from the musical score in terms of timing, dynamics and phrasing. These principles aim to model aspects of renditions in a formal quantitative and predictive way (Widmer & Goebl, 2004). Modelling of performances has also been used for identify the performer of a musical work (Molina-Solana et al., 2008; Saunders et al., 2008). Aims. The main aim of this work is presenting a particular face of the nature vs. culture debate, applying it to music performance. Nature and culture are matched respectively with the structure of the piece and the intentions of the performer. We focus on ritardando performance as it is a commonly studied resource in music performance research. Main contribution. We present in this work the two traditional visions for the role of performers in music performance. These two alternatives can be seen as a particular case of the nature versus culture debate. The first vision considers that performances are shaped by the structure of the piece, with the performer being a mere transmitter. The second one claims that performers do have a more active role, with the obligation of shaping the

virtual environments, and information display applications need dynamic sound models rather than faithful audio reproductions. This implies three levels of research: auditory perception, physicsbased sound modeling, and expressive parametric control. Parallel progress along these three lines leads to effective auditory displays that can complement or substitute visual displays.

The control of sound synthesis is a well-known problem. This is particularly true if the sounds are generated with physical modeling techniques that typically need specification of numerous control parameters. In the present work outcomes from studies on automatic music performance are used for tackling this problem.

this article will review existing representations of timing and tempo common in computational models of music cognition and in programming languages for music. Their differences are discussed, and some refinements will be proposed (referred to as time maps, or TMs). The second part presents an alternative representation and model for time transformation: so-called timing functions (TIFs, an acronym chosen to distinguish them from TFs, or time functions, described by Desain and Honing 1992). This knowledge representation differs in two important aspects from earlier proposals. First, expressive timing is seen as a combination of a tempo component (expressing the change of rate over a fragment of music), and a timing (or time-shift) component that describes how events are timed (e.g., early or late) with respect to this tempo description. Second, expressive timing can be specified in relation to the temporal structure (e.g., position in the phrase or measure), as well as in terms of performance-time, score-time, and global tempo. In addition, TIFs support compositionality (how simple descriptions can be combined into more complex ones) and maintain consistency over musical transformations (how these descriptions of timing and tempo should adapt when other parts of the representation change), both important design criteria of the formalism. Another design criterion is that timing transformations (e.g., the application of an expressive timing model to a score representation) are part of the representation, instead of only acting on a score--- the difference between a knowledge representation and a data representation. To realize this, it is crucial to have access to the timing transformations themselves, not only to the result of their applica- From Time to Time: The Repre...

Abstract — “It’s not what you play, but how you play it”. The term “robotic performance ” has traditionally been used to describe a musical performance that lacks expression and evokes no emotion from listeners. Indeed, current instrumentplaying robots have achieved high technical proficiency, but “perfect ” performances of a piece are not necessarily considered musical. In this paper, we propose a Programming by Playing approach which gives musical expression to robot performances. We further examine precisely what makes music robots play more or less “robotically”, and survey the field of musical expression in search of a good model to make robots play more like humans. I.

Xenakis, and Pietro Grossi, among a few others, started to gain access to computers to make music. It soon became clear that to render music with a socalled “human feel, ” computers needed to process information about performance (e.g., deviations in tempo and loudness), in addition to the symbols that are normally found in a traditional musical score (e.g., pitch and rhythm). This was especially relevant for those interested in using the computer to play back scores. Indeed, the first ever attempt at creating a computer-music programming language, by Max Mathews at Bell Telephone Laboratories in 1957, was motivated by his wish to “write a program to perform music on the computer ” (Park 2009 p. 10). It appears that this development began after Mathews and John Pierce went to a piano concert together. During the intermission, Pierce suggested that perhaps a computer could perform as well as the pianist. Mathews took up the challenge, which resulted in Music I, the ancestor of music programming languages such as Csound (Boulanger 2000). Research into computational models of expressive performance of music (Widmer and Goebl 2004) is still an active area of study—particularly, research into devising increasingly more sophisticated automated and semi-automated computer systems for expressive music performance, hereinafter referred to as CSEMP. A CSEMP is able to generate expressive performances of music. For example, software for music typesetting is often used to write a piece of music, but most packages play back the music in a

Expressive timing is vital for the aesthetic quality that makes us appreciate performed music. It is a largely tacit skill that musicians acquire by practice. A long-standing intuition is that expressive timing is closely related to the concept of motion. This view leads naturally to the adoption of a dynamical systems approach to the study of expressive timing. A well-known visualization technique from dynamical systems theory is the phase-plane representation. The application of this technique, that highlights the dynamic aspects of the data, is demonstrated in a case study on the final ritard in performances of Schumann’s Träumerei. We argue that expressive gestures are visible in a clear and intuitive manner in the phase-plane representations. Another striking aspect of the phase-plane trajectories is their suggestion of human gestural motion. I. INTRODUCTION AND RELATED WORK

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