This article presents a method of adjusting the tempo of a music software sequencer so that it remains synchronized with a drummer's musical pulse. This allows music sequencer technology to be integrated into a band scenario without the compromise of using click tracks or triggering loops with a fixed tempo. Our design implements real-time mechanisms for both underlying tempo and phase adjustment using adaptable parameters that control its behavior. The aim is to create a system that responds to timing variations in the drummer's playing but is also stable during passages of syncopation and fills. We present an evaluation of the system using a stochastic drum machine that incorporates a level of noise in the underlying tempo and phase of the beat. We measure synchronization error between the output of the system and the underlying pulse of the drum machine and contrast this with other real-time beat trackers. The software, B-Keeper, has been released as a Max for Live device, available online at www.b-keeper.org .

In this article, we consider the possibility of mixing two main paradigms of electroacoustic music: the writing-oriented and the performance-oriented paradigms. We show that these two opposing paradigms are the consequence of two corresponding conceptions of time. In addition, we assume that the temporal aspects of a performer's interpretation of a musical composition can be linked to both paradigms. Based on this theoretical study, we propose a formalism for composing pieces of electroacoustic music that can be interpreted in performance.

Telematic musical performance, in which performers at two or more sites collaborate via networked audio and video, suffers significantly from latency. In the extreme case, performers at all sites slow to match their delayed counterparts, resulting in a steadily decreasing tempo. Introducing video of a conductor does not immediately solve the problem, as conductor video is also subjected to network latencies. This article lays the groundwork for an alternative approach to mitigating the effects of latency in distributed orchestral performances, based on generation of a predicted version of the conductor's baton trajectory. The prediction step is the most fundamental problem in this scheme, for which we propose the use of conventional machine learning techniques. Specifically, we demonstrate a particle filter and an extended Kalman filter that each track the location of the baton's tip and predict it multiple beats into the future; we compare these with a conventional feature-based method. We also describe a generic two-part framework that prescribes the incorporation of rehearsal data into a probabilistic model, which is then adapted during live performance. Finally, we suggest a framework and experimental methodology for establishing perceptually based metrics for predicted baton paths. Note that the perceptual efficacy of the presented methods requires experimental confirmation beyond the scope of this article.