195 research outputs found
A latent rhythm complexity model for attribute-controlled drum pattern generation
AbstractMost music listeners have an intuitive understanding of the notion of rhythm complexity. Musicologists and scientists, however, have long sought objective ways to measure and model such a distinctively perceptual attribute of music. Whereas previous research has mainly focused on monophonic patterns, this article presents a novel perceptually-informed rhythm complexity measure specifically designed for polyphonic rhythms, i.e., patterns in which multiple simultaneous voices cooperate toward creating a coherent musical phrase. We focus on drum rhythms relating to the Western musical tradition and validate the proposed measure through a perceptual test where users were asked to rate the complexity of real-life drumming performances. Hence, we propose a latent vector model for rhythm complexity based on a recurrent variational autoencoder tasked with learning the complexity of input samples and embedding it along one latent dimension. Aided by an auxiliary adversarial loss term promoting disentanglement, this effectively regularizes the latent space, thus enabling explicit control over the complexity of newly generated patterns. Trained on a large corpus of MIDI files of polyphonic drum recordings, the proposed method proved capable of generating coherent and realistic samples at the desired complexity value. In our experiments, output and target complexities show a high correlation, and the latent space appears interpretable and continuously navigable. On the one hand, this model can readily contribute to a wide range of creative applications, including, for instance, assisted music composition and automatic music generation. On the other hand, it brings us one step closer toward achieving the ambitious goal of equipping machines with a human-like understanding of perceptual features of music
DMRN+16: Digital Music Research Network One-day Workshop 2021
DMRN+16: Digital Music Research Network One-day Workshop 2021 Queen Mary University of London Tuesday 21st December 2021 Keynote speakers Keynote 1. Prof. Sophie Scott -Director, Institute of Cognitive Neuroscience, UCL. Title: "Sound on the brain - insights from functional neuroimaging and neuroanatomy" Abstract In this talk I will use functional imaging and models of primate neuroanatomy to explore how sound is processed in the human brain. I will demonstrate that sound is represented cortically in different parallel streams. I will expand this to show how this can impact on the concept of auditory perception, which arguably incorporates multiple kinds of distinct perceptual processes. I will address the roles that subcortical processes play in this, and also the contributions from hemispheric asymmetries. Keynote 2: Prof. Gus Xia - Assistant Professor at NYU Shanghai Title: "Learning interpretable music representations: from human stupidity to artificial intelligence" Abstract Gus has been leading the Music X Lab in developing intelligent systems that help people better compose and learn music. In this talk, he will show us the importance of music representation for both humans and machines, and how to learn better music representations via the design of inductive bias. Once we got interpretable music representations, the potential applications are limitless
A systematic review of artificial intelligence-based music generation: Scope, applications, and future trends
Currently available reviews in the area of artificial intelligence-based music generation do not provide a wide range of publications and are usually centered around comparing very specific topics between a very limited range of solutions. Best surveys available in the field are bibliography sections of some papers and books which lack a systematic approach and limit their scope to only handpicked examples In this work, we analyze the scope and trends of the research on artificial intelligence-based music generation by performing a systematic review of the available publications in the field using the Prisma methodology. Furthermore, we discuss the possible implementations and accessibility of a set of currently available AI solutions, as aids to musical composition. Our research shows how publications are being distributed globally according to many characteristics, which provides a clear picture of the situation of this technology.
Through our research it becomes clear that the interest of both musicians and computer scientists in AI-based automatic music generation has increased significantly in the last few years with an increasing participation of mayor companies in the field whose works we analyze. We discuss several generation architectures, both from a technical and a musical point of view and we highlight various areas were further research is needed
National Music Education Standards and Adherence to Bloom\u27s Revised Taxonomy
Pressures from education reforms have contributed to the need for music educators to embrace new and diverse instructional strategies to enhance the learning environment. Music teachers need to understand the pedagogy of teaching and learning and how these affect their praxis. The purpose of this multiple case evaluative study was to investigate the instructional methods used in 10 middle school general music programs to assist students in obtaining the National Standards for Music Education. Bloom\u27s revised taxonomy was the theoretical framework used to evaluate the teaching praxis of the participating teachers. The research questions for the study addressed the effectiveness of the instructional strategies in the music classroom and how they align with the National Standards Music Education and Bloom\u27s Revised Taxonomy. Data were collected from an open ended survey, individual interviews, and unobtrusive documents from 10 general music teachers from suburban, rural, and urban school districts. A line-by-line analysis was followed by a coding matrix to categorize collected data into themes and patterns. The results indicated that standards-based metacognitive instructional strategies can assist music teachers in their classrooms and unite cognitive, affective, and kinesthetic experiences applicable beyond the music classroom. It is recommended that music teachers use alternative teaching techniques to promote and connect critical thinking skills through musical learning experiences. Implications for positive social change include training music educators to create learning environments that support and motivate students to learn and achieve academic success
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Musical expertise as a scaffold for novice programming
textThis study addresses the role of musical expertise on novice computer programming. Engaging novices with computer programming is one of the great challenges of computer science education. Although there is extensive research focusing on constructionist approaches to programming education and creative entry points to programming, little research addresses the topic of how musical expertise informs an unstructured programming activity. To answer this question I focused on the role of participant talk during programming, patterns in participant programming, and evidence of computational thinking in participants’ final Scratch projects.
For this interpretivist study, I worked with a dozen novice programmers from a variety of musical backgrounds: classical musicians, jazz musicians, composers, and non- musicians. Each participant worked on a free-form musical project in the Scratch programming environment. I collected data including participant talk, screen recordings of participant programming, and participants’ final Scratch projects.
Overall, musical participants more readily took to the numeracy involved in programming music in Scratch. Also, musical participants were able to use musical concepts and techniques as jumping-off points for programming challenges. Considering my results by participant group, composers stood out in a number of ways: working the
longest, testing their programs the most often, adding Scratch objects the slowest, v
removing the most Scratch objects, creating projects of the greatest nested depth, and unanimous use of operators and random numbers. Non-musicians, on the other hand, worked for the shortest amount of time, added the fewest Scratch objects, and created projects of the lowest nested depth.
In addition to adding to the body of research around chunking and tinkering, this study reinforces the importance of context and comfort in an introduction to computer programming. Composition may be an especially rich area to leverage, given the design- like programming activity of the composers here. Future research projects could resemble this one while focusing on younger learners, explicit musical concepts like those invoked by participants, or alternative performing arts framings such as theater or dance.Curriculum and Instructio
Embodying an Interactive AI for Dance Through Movement Ideation
What expectations exist in the minds of dancers when interacting with a generative machine learning model? During two workshop events, experienced dancers explore these expectations through improvisation and role-play, embodying an imagined AI-dancer. The dancers explored how intuited flow, shared images, and the concept of a human replica might work in their imagined AI-human interaction. Our findings challenge existing assumptions about what is desired from generative models of dance, such as expectations of realism, and how such systems should be evaluated. We further advocate that such models should celebrate non-human artefacts, focus on the potential for serendipitous moments of discovery, and that dance practitioners should be included in their development. Our concrete suggestions show how our findings can be adapted into the development of improved generative and interactive machine learning models for dancers’ creative practice
Data-Driven Query by Vocal Percussion
The imitation of percussive sounds via the human voice is a natural and effective tool for communicating rhythmic ideas on the fly. Query by Vocal Percussion (QVP) is a subfield in Music Information Retrieval (MIR) that explores techniques to query percussive sounds using vocal imitations as input, usually plosive consonant sounds. In this way, fully automated QVP systems can help artists prototype drum patterns in a comfortable and quick way, smoothing the creative workflow as a result. This project explores the potential usefulness of recent data-driven neural network models in two of the most important tasks in QVP. Algorithms relative to Vocal Percussion Transcription (VPT) detect and classify vocal percussion sound events in a beatbox-like performance so to trigger individual drum samples. Algorithms relative to Drum Sample Retrieval by Vocalisation (DSRV) use input vocal imitations to pick appropriate drum samples from a sound library via timbral similarity. Our experiments with several kinds of data-driven deep neural networks suggest that these achieve better results in both VPT and DSRV compared to traditional data-informed approaches based on heuristic audio features. We also find that these networks, when paired with strong regularisation techniques, can still outperform data-informed approaches when data is scarce. Finally, we gather several insights relative to people’s approach to vocal percussion and how user-based algorithms are essential to better model individual differences in vocalisation styles
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