The advent of quantum computer music: mapping the field.

Quantum computing technology is developing at a fast pace. The impact of quantum computing on the music industry is inevitable. This paper maps the emerging field of quantum computer music. Quantum computer music investigates, and develops applications and methods to process music using quantum computing technology. The paper begins by contextualising the field. Then, it discusses significant examples of various approaches developed to date to leverage quantum computing to learn, process and generate music. The methods discussed range from rendering music using data from physical quantum mechanical systems and quantum mechanical simulations to computational quantum algorithms to generate music, including quantum AI. The ambition to develop techniques to encode audio quantumly for making sound synthesisers and audio signal processing systems is also discussed.

Preferred music listening for people living with dementia: Two home-based case studies discussing compilation process, autobiographical and biophysical responses.

Neuropsychiatric symptoms (NPS) in dementia can be reduced through music listening. Little has been reported on home-based listening, compilation processes, or individual responses that include biophysiological data. We aim to provide new insights from two home-based case studies focused on specific music selections. Participants were part of a larger study, co-designing an automated radio, diary reminder and personal playlist system for NPS management. Playlists were compiled that would have the best possible chance of achieving this, based on participants' autobiographical, narrative, heart rate (HR) and videoed responses. Participant's responses to their music aligned with the genre they chose - dancing to up-beat music, contrasting with subtle responses to Beethoven. Repeated listens may help to establish consistency of responses and allow time to communicate their genuine preferences, not those others suggested. If all of these data converge, then they could help confirm the suitability of music for NPS management.

Genetic Music System with Synthetic Biology.

This article introduces GeMS, a system for music composition informed by synthetic biology. GeMS generates music with simulations of genetic processes, such as transcription, translation, and protein folding, with which biological systems render chains of amino acids from DNA strands. The system comprises the following components: the Miranda machine, the rhythmator, and the pitch processor. The Miranda machine is an abstract Turing-machine-like processor, which manipulates a sequence of DNA symbols according to a set of programming instructions. This process generates a pool of new DNA strands, which are subsequently translated into rhythms. GeMS represents the musical equivalent of amino acids in terms of rhythms, referred to as rhythmic codons. This enables the rhythmator to convert DNA sequences into rhythmic sequences. The pitch processor generates pitches for such rhythmic sequences. It is inspired by the phenomenon of protein folding. The pitch processor considers orientation information of DNA instructions yielded by the Miranda machine in order to activate algorithms for generating pitches. A musical composition, entitled Artibiotics, for percussion ensemble and electronic instruments, is presented to demonstrate the system.

SSVEP-based brain-computer interface for music using a low-density EEG system.

In this paper, we present a bespoke brain-computer interface (BCI), which was developed for a person with severe motor-impairments, who was previously a Violinist, to allow performing and composing music at home. It uses steady-state visually evoked potential (SSVEP) and adopts a dry, low-density, and wireless electroencephalogram (EEG) headset. In this study, we investigated two parameters: (1) placement of the EEG headset and (2) inter-stimulus distance and found that the former significantly improved the information transfer rate (ITR). To analyze EEG, we adopted canonical correlation analysis (CCA) without weight-calibration. The BCI for musical performance realized a high ITR of 37.59 ± 9.86 bits min-1 and a mean accuracy of 88.89 ± 10.09%. The BCI for musical composition obtained an ITR of 14.91 ± 2.87 bits min-1 and a mean accuracy of 95.83 ± 6.97%. The BCI was successfully deployed to the person with severe motor-impairments. She regularly uses it for musical composition at home, demonstrating how BCIs can be translated from laboratories to real-world scenarios.

A Method for Growing Bio-memristors from Slime Mold.

Our research is aimed at gaining a better understanding of the electronic properties of organisms in order to engineer novel bioelectronic systems and computing architectures based on biology. This specific paper focuses on harnessing the unicellular slime mold Physarum polycephalum to develop bio-memristors (or biological memristors) and bio-computing devices. The memristor is a resistor that possesses memory. It is the 4th fundamental passive circuit element (the other three are the resistor, the capacitor, and the inductor), which is paving the way for the design of new kinds of computing systems; e.g., computers that might relinquish the distinction between storage and a central processing unit. When applied with an AC voltage, the current vs. voltage characteristic of a memristor is a pinched hysteresis loop. It has been shown that P. polycephalum produces pinched hysteresis loops under AC voltages and displays adaptive behavior that is comparable with the functioning of a memristor. This paper presents the method that we developed for implementing bio-memristors with P. polycephalum and introduces the development of a receptacle to culture the organism, which facilitates its deployment as an electronic circuit component. Our method has proven to decrease growth time, increase component lifespan, and standardize electrical observations.

At the crossroads of evolutionary computation and music: self-programming synthesizers, swarm orchestras and the origins of melody.

This paper introduces three approaches to using Evolutionary Computation (EC) in Music (namely, engineering, creative and musicological approaches) and discusses examples of representative systems that have been developed within the last decade, with emphasis on more recent and innovative works. We begin by reviewing engineering applications of EC in Music Technology such as Genetic Algorithms and Cellular Automata sound synthesis, followed by an introduction to applications where EC has been used to generate musical compositions. Next, we introduce ongoing research into EC models to study the origins of music and detail our own research work on modelling the evolution of melody.