Pure Data

Abstract: Designing Sound teaches students and professional sound designers to understand and create sound effects starting from nothing. Its thesis is that any sound can be generated from first principles, guided by analysis and synthesis. The text takes a practitioner's perspective, exploring the basic principles of making ordinary, everyday sounds using an easily accessed free software. Readers use the Pure Data (Pd) language to construct sound objects, which are more flexible and useful than recordings. Sound is considered as a process, rather than as dataan approach sometimes known as "procedural audio." Procedural sound is a living sound effect that can run as computer code and be changed in real time according to unpredictable events. Applications include video games, film, animation, and media in which sound is part of an interactive process. The book takes a practical, systematic approach to the subject, teaching by example and providing background information that offers a firm theoretical context for its pragmatic stance. Many of the examples follow a pattern, beginning with a discussion of the nature and physics of a sound, proceeding through the development of models and the implementation of examples, to the final step of producing a Pure Data program for the desired sound. Different synthesis methods are discussed, analyzed, and refined throughout. After mastering the techniques presented in Designing Sound, students will be able to build their own sound objects for use in interactive applications and other projects.

Abstract: In this paper, we describe an adaptive approach to gesture mapping for musical applications which serves as a mapping system for music instrument design. A neural network approach is chosen for this goal and all the required interfaces and abstractions are developed and demonstrated in the Pure Data environment. In this paper, we will focus on neural network representation and implementation in a real-time musical environment. This adaptive mapping is evaluated in different static and dynamic situations by a network of sensors sampled at a rate of 200Hz in real-time. Finally, some remarks are given on the network design and future works.

Abstract: This paper documents the development of ml.lib: a set of open-source tools designed for employing a wide range of machine learning techniques within two popular real-time programming environments, namely Max and Pure Data. ml.lib is a cross-platform, lightweight wrapper around Nick Gillian's Gesture Recognition Toolkit, a C++ library that includes a wide range of data processing and machine learning techniques. ml.lib adapts these techniques for real-time use within popular data-flow IDEs, allowing instrument designers and performers to integrate robust learning, classification and mapping approaches within their existing workflows. ml.lib has been carefully de-signed to allow users to experiment with and incorporate ma-chine learning techniques within an interactive arts context with minimal prior knowledge. A simple, logical and consistent, scalable interface has been provided across over sixteen exter-nals in order to maximize learnability and discoverability. A focus on portability and maintainability has enabled ml.lib to support a range of computing architectures - including ARM - and operating systems such as Mac OS, GNU/Linux and Win-dows, making it the most comprehensive machine learning implementation available for Max and Pure Data.

Abstract: Many applications from scientific computing and physical simulations can benefit from a mixed task and data parallel implementation on parallel machines with a distributed memory organization, but it may also be the case that a pure data parallel implementation leads to faster execution times. Since the effort for writing a mixed task and data parallel implementation is large, it would be useful to have an a priori estimation of the possible benefits of such an implementation on a given parallel machine. In this article, we propose an estimation method for the execution time that is based on the modelling of computation and communication times by runtime formulas. The effect of concurrent message transmissions is captured by a contention factor for the specific target machine. To demonstrate the usefulness of the approach, we consider a complex method for the solution of ordinary differential equations with a potential for a mixed task and data parallel execution. As distributed memory machine we consider the Cray T3E and a Linux cluster.