Focuses on the role of the computer as a generative tool for music composition. Miranda introduces a number of computer music composition techniques ranging from probabilities, formal grammars and fractals, to genetic algorithms, cellular automata and neural computation. Anyone wishing to use the computer as a companion to create music will find this book a valuable resource. As a comprehensive guide with full explanations of technical terms, it is suitable for students, professionals and enthusiasts alike. The accompanying CD-ROM contains examples, complementary tutorials and a number of composition systems for PC and Macintosh platforms, from demonstration versions of commercial programs to exciting, fully working packages developed by research centres world-wide, including Nyquist, Bol Processor, Music Sketcher, SSEYO Koan, Open Music and the IBVA brainwaves control system, among others. This book will be interesting to anyone wishing to use the computer as a companion to create music. It is a comprehensive guide, but the technical terms are explained so it is suitable for students, professionals and enthusiasts alike.

Audio segmentation and sound event detection are crucial topics in machine listening that aim to detect acoustic classes and their respective boundaries. It is useful for audio-content analysis, speech recognition, audio-indexing, and music information retrieval. In recent years, most research articles adopt segmentation-by-classification. This technique divides audio into small frames and individually performs classification on these frames. In this paper, we present a novel approach called You Only Hear Once (YOHO), which is inspired by the YOLO algorithm popularly adopted in Computer Vision. We convert the detection of acoustic boundaries into a regression problem instead of frame-based classification. This is done by having separate output neurons to detect the presence of an audio class and predict its start and end points. The relative improvement for F-measure of YOHO, compared to the state-of-the-art Convolutional Recurrent Neural Network, ranged from 1% to 6% across multiple datasets for audio segmentation and sound event detection. As the output of YOHO is more end-to-end and has fewer neurons to predict, the speed of inference is at least 6 times faster than segmentation-by-classification. In addition, as this approach predicts acoustic boundaries directly, the post-processing and smoothing is about 7 times faster.

This paper reports on the development of a proof-of-concept brain-computer music interfacing system (BCMI), which we built to be tested with a patient with Locked-in Syndrome at the Royal Hospital for Neuro-disability, in London. The system uses the Steady State Visual Evoked Potential (SSVEP) method, whereby targets are presented to a user on a computer monitor representing actions available to perform with the system. Each target is encoded by a flashing visual pattern reversing at a unique frequency. In order to make a selection, the user must direct her gaze at the target corresponding to the action she would like to perform. The patient grasped the concept quickly and rapidly demonstrated her skill at controlling the system with minimal practice. She was able to vary the intensity of her gaze, thus changing the amplitude of her EEG and vary the consequent musical parameters. We have proved the concept that such a BCMI system is cost-effective to build, viable, and useful. However, ergonomic and design aspects of the system require further refinement in order to make it more practical for clinical usage. For instance, the system at present requires a therapist to place individual electrodes and calibrate a user’s response to each stimulus, which can be time consuming. A new version of the system will require just positioning of a headset and, due to advanced algorithms, will require no calibration.

We present a survey of research into automated and semiautomated computer systems for expressive performance of music. We will examine the motivation for such systems and then examine the majority of the systems developed over the last 25 years. To highlight some of the possible future directions for new research, the review uses primary terms of reference based on four elements: testing status, expressive representation, polyphonic ability, and performance creativity.

The authors discuss their work on developing technology to interface the brain directly with music systems, a field of research generally known as Brain-Computer Interfacing (BCI). The paper gives a brief background of BCI in general and surveys various attempts at musical BCI, or Brain-Computer Music Interface (BCMI) — systems designed to make music from brain signals, or brainwaves. The authors present a technical introduction to the electroencephalogram (EEG), which measures brainwaves detected by electrodes placed directly on the scalp. They introduce approaches to the design of BCI and BCMI systems and present two case study systems of their own design: the BCMI-Piano and the Inter-Harmonium.