Performer Experience on a Continuous Keyboard Instrument

On several keyboard instruments the produced sound is not always dependent exclusively on a discrete key-velocity parameter, and minute gestural details can affect the final sonic result. By contrast, variations in articulation beyond velocity have normally no effect on the produced sound when the keyboard controller uses the MIDI standard, used in the vast majority of digital keyboards. In this article, we introduce a novel keyboard-based digital musical instrument that uses continuous readings of key position to control a nonlinear waveguide flute synthesizer with a richer set of interaction gestures than would be possible with a velocity-based keyboard. We then report on the experience of six players interacting with our instrument and reflect on their experience, highlighting the opportunities and challenges that come with continuous key sensing

Super Size Me: Interface Size, Identity and Embodiment in Digital Musical Instrument Design

Digital interfaces are shrinking, driven by pressures of mass production and consumer culture, and often accompanied by a discourse of control, precision or convenience. Meanwhile, human bodies remain the same size, and the changing size of interfaces has implications for the formation of user identities. Drawing on embodied cognition, effort and entanglement theories of HCI, we explored the impact of interface size on the co-constitution of humans and technology. We designed an oversized digital musical instrument and invited musicians to use the instrument to create original performances. We found that both the performances and the musicians' self-perception were influenced by the large size of the instrument, shining new light on the ways in which designing technology is designing humans and in turn culture

Embodied Cognition in Performers of Large Acoustic Instruments as a Method of Designing New Large Digital Musical Instruments

We present The Large Instrument Performers Study, an interview-based exploration into how large scale acoustic instrument performers navigate the instrument's size-related aesthetic features during the performance. Through the conceptual frameworks of embodied music cognition and affordance theory, we discuss how the themes that emerged in the interview data reveal the ways size-related aesthetic features of large acoustic instruments influence the instrument performer's choices; how large scale acoustic instruments feature microscopic nuanced performance options; and how despite the preconception of large scale acoustic instruments being scaled up versions of the smaller instrument with the addition of a lower fundamental tone, the instruments o er different sonic and performative features to their smaller counterparts and require precise gestural control that is certainly not scaled up. This is followed by a discussion of how the study findings could influence design features in new large scale digital musical instruments to result in more nuanced control and timbrally rich instruments, and better understanding of how interfaces and instruments influence performers' choices and as a result music repertoire and performance

Singing Knit: Soft Knit Biosensing for Augmenting Vocal Performances

This paper discusses the design of the Singing Knit, a wearable knit collar for measuring a singer's vocal interactions through surface electromyography. We improve the ease and comfort of multi-electrode bio-sensing systems by adapting knit e-textile methods. The goal of the design was to preserve the capabilities of rigid electrode sensing while addressing its shortcomings, focusing on comfort and reliability during extended wear, practicality and convenience for performance settings, and aesthetic value. We use conductive, silver-plated nylon jersey fabric electrodes in a full rib knit accessory for sensing laryngeal muscular activation. We discuss the iterative design and the material decision-making process as a method for building integrated soft-sensing wearable systems for similar settings. Additionally, we discuss how the design choices through the construction process reflect its use in a musical performance context

Adapting the Bass Guitar for One-Handed Playing

This paper presents a prototype system for adapting the bass guitar for one-handed musicians. We discuss existing solutions to accessible musical instruments, followed by the results of an online survey of bass guitarists, which informed the design of a prototype bass guitar adaptation. The adaptation comprises a foot-operated MIDI controller with a solenoid-actuated fretting mechanism, providing access to six frets across two strings of the bass. A study involving six bassists rehearsing and writing a bass guitar accompaniment with the adapted bass highlighted unexpected facets of bass guitar playing, and provided insights into the design of future accessible string instruments

Skip the Pre-Concert Demo: How Technical Familiarity and Musical Style Affect Audience Response

This paper explores the roles of technical and musical familiarity in shaping audience response to digital musical instrument (DMI) performances. In an audience study conducted during an evening concert, we examined two primary questions: first, whether a deeper understanding of how a DMI works increases an audience’s enjoyment and interest in the performance; and second, given the same DMI and same performer, whether playing in a conventional (vernacular) versus an experimental musical style affects an audience’s response. We held a concert in which two DMI creator-performers each played two pieces in differing styles. Before the concert, each half the 64-person audience was given a technical explanation of one of the instruments. Results showed that receiving an explanation increased the reported understanding of that instrument, but had no effect on either the reported level of interest or enjoyment. On the other hand, performances in experimental versus conventional style on the same instrument received widely divergent audience responses. We discuss implications of these findings for DMI design

Making High-Performance Embedded Instruments with Bela and Pure Data

This hands-on workshop introduces participants to Bela, an embedded platform for ultra-low latency audio and sensor processing.This hands-on workshop introduces participants to Bela, an embedded platform for ultra-low latency audio and sensor processing.This hands-on workshop introduces participants to Bela, an embedded platform for ultra-low latency audio and sensor processing.This hands-on workshop introduces participants to Bela, an embedded platform for ultra-low latency audio and sensor processing.This hands-on workshop introduces participants to Bela, an embedded platform for ultra-low latency audio and sensor processing.This hands-on workshop introduces participants to Bela, an embedded platform for ultra-low latency audio and sensor processing.This hands-on workshop introduces participants to Bela, an embedded platform for ultra-low latency audio and sensor processing.Bela is an embedded platform for ultra-low latency audio and sensor processing. We present here the hardware and software features of Bela with particular focus on its integration with Pure Data. Sensor inputs on Bela are sampled at audio rate, which opens to the possibility of doing signal processing using Pure Data’s audio-rate objects

Real-time Percussive Technique Recognition and Embedding Learning for the Acoustic Guitar

Real-time music information retrieval (RT-MIR) has much potential to augment the capabilities of traditional acoustic instruments. We develop RT-MIR techniques aimed at augmenting percussive fingerstyle, which blends acoustic guitar playing with guitar body percussion. We formulate several design objectives for RT-MIR systems for augmented instrument performance: (i) causal constraint, (ii) perceptually negligible action-to-sound latency, (iii) control intimacy support, (iv) synthesis control support. We present and evaluate real-time guitar body percussion recognition and embedding learning techniques based on convolutional neural networks (CNNs) and CNNs jointly trained with variational autoencoders (VAEs). We introduce a taxonomy of guitar body percussion based on hand part and location. We follow a cross-dataset evaluation approach by collecting three datasets labelled according to the taxonomy. The embedding quality of the models is assessed using KL-Divergence across distributions corresponding to different taxonomic classes. Results indicate that the networks are strong classifiers especially in a simplified 2-class recognition task, and the VAEs yield improved class separation compared to CNNs as evidenced by increased KL-Divergence across distributions. We argue that the VAE embedding quality could support control intimacy and rich interaction when the latent space's parameters are used to control an external synthesis engine. Further design challenges around generalisation to different datasets have been identified