Live coding machine learning and machine listening: a survey on the design of languages and environments for live coding

The MIMIC (Musically Intelligent Machines Interacting Creatively) project explores how the techniques of machine learning and machine listening can be communicated and implemented in simple terms for composers, instrument makers and performers. The potential for machine learning to support musical composition and performance is high, and with novel techniques in machine listening, we see emerging a technology that can shift from being instrumental to conversational and collaborative. By leveraging the internet as a live software ecosystem, the MIMIC project explores how such technology can best reach artists, and live up to its collaborative potential to fundamentally change creative practice in the field. The project involves creating a high level language that can be used for live coding, creative coding and quick prototyping. Implementing a language that interfaces with technically complex problems such as the design of machine learning neural networks or the temporal and spectral algorithms applied in machine listening is not a simple task, but we can build upon decades of research and practice in programming language design (Ko 2016), and computer music language design in particular, as well as a plethora of inventive new approaches in the design of live coding systems for music (Reina et al. 2019). The language and user interface design will build on recent research in creative coding and interactive machine learning, exemplified by the Rapid Mix project (Bernardo et. al., 2016, Zbyszynski et. al., 2017). Machine learning continues to be at the forefront of new innovations in computer music, (e.g. new sound synthesis techniques in NSynth (Engel et. al. 2017) and WaveNet (van den Oord, 2016)); the language will seek to integrate models based around these new techniques into live coding performance, and also explore the efficacy of live coding as an approach to training and exploiting these systems for analysing and generating sound. Existing live coding systems and languages are often reported on, describing clever solutions as well as weaknesses, as given, for example, in accounts of the development of Tidal (McLean, 2014), Extramuros (Ogborn et. al, 2015) and Gibber (Roberts and Kuchera-Morin, 2012). Researchers are typically reflective and openly critical of their own systems when analysing them and often report on its design with wider implications (Aaron 2011; Sorensen 2018). However, they rarely speculate freely and uninhibitedly about possible solutions or alternative paths taken; the focus is typically on the system described. Before defining the design of our own system, we were therefore interested in opening up a channel where we could learn from other practitioners in language design, machine learning and machine listening. We created a survey that we sent out to relevant communities of practice - such as live coding, machine learning, machine listening, creative coding, deep learning - and asked open questions about how they might imagine a future system implemented, given the knowledge we have today. Below we report on the questionnaire and its findings

Proceedings of the 1st International Conference on Live Coding

Open Access peer reviewed papers on live coding published at the 1st International Conference on Live Coding (ICLC) in Leeds

Improving User Involvement Through Live Collaborative Creation

Creating an artifact - such as writing a book, developing software, or performing a piece of music - is often limited to those with domain-specific experience or training. As a consequence, effectively involving non-expert end users in such creative processes is challenging. This work explores how computational systems can facilitate collaboration, communication, and participation in the context of involving users in the process of creating artifacts while mitigating the challenges inherent to such processes. In particular, the interactive systems presented in this work support live collaborative creation, in which artifact users collaboratively participate in the artifact creation process with creators in real time. In the systems that I have created, I explored liveness, the extent to which the process of creating artifacts and the state of the artifacts are immediately and continuously perceptible, for applications such as programming, writing, music performance, and UI design. Liveness helps preserve natural expressivity, supports real-time communication, and facilitates participation in the creative process. Live collaboration is beneficial for users and creators alike: making the process of creation visible encourages users to engage in the process and better understand the final artifact. Additionally, creators can receive immediate feedback in a continuous, closed loop with users. Through these interactive systems, non-expert participants help create such artifacts as GUI prototypes, software, and musical performances. This dissertation explores three topics: (1) the challenges inherent to collaborative creation in live settings, and computational tools that address them; (2) methods for reducing the barriers of entry to live collaboration; and (3) approaches to preserving liveness in the creative process, affording creators more expressivity in making artifacts and affording users access to information traditionally only available in real-time processes. In this work, I showed that enabling collaborative, expressive, and live interactions in computational systems allow the broader population to take part in various creative practices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145810/1/snaglee_1.pd

The practices of programming

© 2016 IEEE.How diverse are the ways that programming is done? While a variety of accounts exist, each appears in isolation, neither framed in terms of a distinct practice, nor as one of many such practices. In this work we explore accounts spanning software engineering, bricolage/tinkering, sketching, live coding, code-bending, and hacking. These practices of programming are analyzed in relation to ongoing research, and in particular HCI's 'practice turn', offering connections to accounts of practice in other contexts than programming. The conceptualization of practice helps to interpret recent interest in program code as craft material, and also offers potential to inform programming education, tools and work as well as future research

Torino: A Tangible Programming Language Inclusive of Children with Visual Disabilities

© 2018, Copyright © 2018 Taylor & Francis Group, LLC. Across the world, policy initiatives are being developed to engage children with computer programming and computational thinking. Diversity and inclusion has been a strong force in this agenda, but children with disabilities have largely been omitted from the conversation. Currently, there are no age appropriate tools for teaching programming concepts and computational thinking to primary school children with visual disabilities. We address this gap through presenting the design and implementation of Torino, a tangible programming language for teaching programming concepts to children age 7–11 regardless of level of vision. In this paper, we: (1) describe the design process done in conjunction with children with visual disabilities; (2) articulate the design decisions made; and (3) report insights generated from an evaluation with 10 children with mixed visual abilities that considers how children are able to trace (read) and create (write) programs with Torino. We discuss key design trade-offs: (1) readability versus extensibility; and (2) size versus liveness. We conclude by reflecting upon how an inclusive design approach shaped the final result

Working Performatively with Interactive 3D Printing: An artistic practice utilising interactive programming for computational manufacturing and livecoding

This thesis explores the liminal space where personal computational art and design practices and mass-manufacturing technologies intersect. It focuses on what it could look and feel like to be a computationally-augmented, creative practitioner working with 3D printing in a more programmatic, interactive way. The major research contribution is the introduction of a future-looking practice of Interactive 3D Printing (I3DP).I3DP is articulated using the Cognitive Dimensions of Notations in terms of associated user activities and design trade-offs. Another contribution is the design, development, and analysis of a working I3DP system called LivePrinter. LivePrinter is evaluated through a series of qualitiative user studies and a personal computational art practice, including livecoding performances and 3D form-making

Supporting virtuosity and flow in computer music

As we begin to realise the sonic and expressive potential of the computer, HCI researchers face the challenge of designing rewarding and accessible user experiences that enable individuals to explore complex creative domains such as music. In performance-based music systems such as sequencers, a disjunction exists between the musician’s specialist skill with performance hardware and the generic usability techniques applied in the design of the software. The creative process is not only fragmented across multiple physical (and virtual) devices, but divided across creativity and productivity phases separated by the act of recording. Integrating psychologies of expertise and intrinsic motivation, this thesis proposes a design shift from usability to virtuosity, using theories of “flow” (Csikszentmihalyi, 1996) and feedback “liveness” (Tanimoto, 1990) to identify factors that facilitate learning and creativity in digital notations and interfaces, leading to a set of design heuristics to support virtuosity in notation use. Using the cognitive dimensions of notations framework (Green, 1996), models of the creative user experience are developed, working towards a theoretical framework for HCI in music systems, and specifically computer-aided composition. Extensive analytical methods are used to look at corollaries of virtuosity and flow in real-world computer music interaction, notably in soundtracking, a software-based composing environment offering a rapid edit-audition feedback cycle, enabled by the user’s skill in manipulating the text-based notation (and program) through the computer keyboard. The interaction and development of more than 1,000 sequencer and tracker users was recorded over a period of 2 years, to investigate the nature and development of skill and technique, look for evidence of flow experiences, and establish the use and role of both visual and musical feedback in music software. Quantitative analyses of interaction data are supplemented with a detailed video study of a professional tracker composer, and a user survey that draws on psychometric methods to evaluate flow experiences in the use of digital music notations, such as sequencers and trackers. Empirical findings broadly support the proposed design heuristics, and enable the development of further models of liveness and flow in notation use. Implications for UI design are discussed in the context of existing music systems, and supporting digitally-mediated creativity in other domains based on notation use

Music feature extraction and analysis through Python

En l'era digital, plataformes com Spotify s'han convertit en els principals canals de consum de música, ampliant les possibilitats per analitzar i entendre la música a través de les dades. Aquest projecte es centra en un examen exhaustiu d'un conjunt de dades obtingut de Spotify, utilitzant Python com a eina per a l'extracció i anàlisi de dades. L'objectiu principal es centra en la creació d'aquest conjunt de dades, emfatitzant una àmplia varietat de cançons de diversos subgèneres. La intenció és representar tant el panorama musical més tendenciós i popular com els nínxols, alineant-se amb el concepte de distribució de Cua Llarga, terme popularitzat com a "Long Tail" en anglès, que destaca el potencial de mercat de productes de nínxols amb menor popularitat. A través de l'anàlisi, es posen de manifest patrons en l'evolució de les característiques musicals al llarg de les dècades passades. Canvis en característiques com l'energia, el volum, la capacitat de ball, el positivisme que desprèn una cançó i la seva correlació amb la popularitat sorgeixen del conjunt de dades. Paral·lelament a aquesta anàlisi, es concep un sistema de recomanació musical basat en el contingut del conjunt de dades creat. L'objectiu és connectar cançons, especialment les menys conegudes, amb possibles oients. Aquest projecte ofereix perspectives beneficioses per a entusiastes de la música, científics de dades i professionals de la indústria. Les metodologies implementades i l'anàlisi realitzat presenten un punt de convergència de la ciència de dades i la indústria de la música en el context digital actualEn la era digital, plataformas como Spotify se han convertido en los principales canales de consumo de música, ampliando las posibilidades para analizar y entender la música a través de los datos. Este proyecto se centra en un examen exhaustivo de un conjunto de datos obtenido de Spotify, utilizando Python como herramienta para la extracción y análisis de datos. El objetivo principal se centra en la creación de este conjunto de datos, enfatizando una amplia variedad de canciones de diversos subgéneros. La intención es representar tanto el panorama musical más tendencioso y popular como los nichos, alineándose con el concepto de distribución de Cola Larga, término popularizado como Long Tail en inglés, que destaca el potencial de mercado de productos de nichos con menor popularidad. A través del análisis, se evidencian patrones en la evolución de las características musicales a lo largo de las décadas pasadas. Cambios en características como la energía, el volumen, la capacidad de baile, el positivismo que desprende una canción y su correlación con la popularidad surgen del conjunto de datos. Paralelamente a este análisis, se concibe un sistema de recomendación musical basado en el contenido del conjunto de datos creado. El objetivo es conectar canciones, especialmente las menos conocidas, con posibles oyentes. Este proyecto ofrece perspectivas beneficiosas para entusiastas de la música, científicos de datos y profesionales de la industria. Las metodologías implementadas y el análisis realizado presentan un punto de convergencia de la ciencia de datos y la industria de la música en el contexto digital actualIn the digital era, platforms like Spotify have become the primary channels of music consumption, broadening the possibilities for analyzing and understanding music through data. This project focuses on a comprehensive examination of a dataset sourced from Spotify, with Python as the tool for data extraction and analysis. The primary objective centers around the creation of this dataset, emphasizing a diverse range of songs from various subgenres. The intention is to represent both mainstream and niche musical landscapes, aligning with the Long Tail distribution concept, which highlights the market potential of less popular niche products. Through analysis, patterns in the evolution of musical features over past decades become evident. Shifts in features such as energy, loudness, danceability, and valence and their correlation with popularity emerge from the dataset. Parallel to this analysis is the conceptualization of a music recommendation system based on the content of the data set. The aim is to connect tracks, especially lesser-known ones, with potential listeners. This project provides insights beneficial for music enthusiasts, data scientists, and industry professionals. The methodologies and analyses present a convergence of data science and the music industry in today's digital contex