54,705 research outputs found
Methods and Prospects for Human-Computer Performance of Popular Music
Computers are often used in performance of popular music, but most often in very restricted ways, such as keyboard synthesizers where musicians are in complete control, or pre-recorded or sequenced music where musicians follow the computer's drums or click track. An interesting and yet little-explored possibility is the computer as highly autonomous performer of popular music, capable of joining a mixed ensemble of computers and humans. Considering the skills and functional requirements of musicians leads to a number of predictions about future humanâcomputer music performance (HCMP) systems for popular music. We describe a general architecture for such systems and describe some early implementations and our experience with them
Straddling the intersection
Music technology straddles the intersection between art and science and presents those who choose to work within its sphere with many practical challenges as well as creative possibilities. The paper focuses on four main areas: secondary education, higher education, practice and research and finally collaboration. The paper emphasises the importance of collaboration in tackling the challenges of interdisciplinarity and in influencing future technological developments
A new approach to onset detection: towards an empirical grounding of theoretical and speculative ideologies of musical performance
This article assesses aspects of the current state of a project which aims, with the help of computers
and computer software, to segment soundfiles of vocal melodies into their component notes, identifying
precisely when the onset of each note occurs, and then tracking the pitch trajectory of each
note, especially in melodies employing a variety of non-standard temperaments, in which musical
intervals smaller than 100 cents are ubiquitous. From there, we may proceed further, to describe
many other âmicro-featuresâ of each of the notes, but for now our focus is on the onset times and
pitch trajectories
BitBox!:A case study interface for teaching real-time adaptive music composition for video games
Real-time adaptive music is now well-established as a popular medium, largely through its use in video game soundtracks. Commercial packages, such as fmod, make freely available the underlying technical methods for use in educational contexts, making adaptive music technologies accessible to students. Writing adaptive music, however, presents a significant learning challenge, not least because it requires a different mode of thought, and tutor and learner may have few mutual points of connection in discovering and understanding the musical drivers, relationships and structures in these works. This article discusses the creation of âBitBox!â, a gestural music interface designed to deconstruct and explain the component elements of adaptive composition through interactive play. The interface was displayed at the Dare Protoplay games exposition in Dundee in August 2014. The initial proof-of- concept study proved successful, suggesting possible refinements in design and a broader range of applications
The Role of Technology in Music Education: a Survey of Computer Usage in Teaching Music in Colleges of Education in The Volta Region, Ghana
The study sought to find out the role of computer technology in music education in Colleges of Education in the Volta Region of Ghana. It aimed at surveying the use of computer technology for teaching music and exploring the instructional prospects for computer technology usage in music in Colleges of Education. The study employed Rogersâ Diffusion Innovation theory and descriptive survey research method. Data was
collected from the respondents using questionnaire, interview, and observation. The study revealed that even though about 90% of the music tutors have good academic qualification and over five years teaching experience, lack of competence in handling computer technology in teaching music among some music tutors and incoherent ICT initiatives hindered proper application of computer technology in the field of music
education. It is however envisaged that increasing access and coherent computer technology initiatives will be paramount for the teaching of music in the Colleges of Education
New Trends in Development of Services in the Modern Economy
The services sector strategic development unites a multitude of economic and managerial aspects and is one of the most important problems of economic management. Many researches devoted to this industry study are available. Most of them are performed in the traditional aspect of the voluminous calendar approach to strategic management, characteristic of the national scientific school. Such an approach seems archaic, forming false strategic benchmarks.
The services sector is of special scientific interest in this context due to the fact that the social production structure to the services development model attraction in many countries suggests transition to postindustrial economy type where the services sector is a system-supporting sector of the economy. Actively influencing the economy, the services sector in the developed countries dominates in the GDP formation, primary capital accumulation, labor, households final consumption and, finally, citizens comfort of living.
However, a clear understanding of the services sector as a hyper-sector permeating all spheres of human activity has not yet been fully developed, although interest in this issue continues to grow among many authors.
Target of strategic management of the industry development setting requires substantive content and the services sector target value assessment
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
Optical Music Recognition (OMR) is an important technology within Music
Information Retrieval. Deep learning models show promising results on OMR
tasks, but symbol-level annotated data sets of sufficient size to train such
models are not available and difficult to develop. We present a deep learning
architecture called a Convolutional Sequence-to-Sequence model to both move
towards an end-to-end trainable OMR pipeline, and apply a learning process that
trains on full sentences of sheet music instead of individually labeled
symbols. The model is trained and evaluated on a human generated data set, with
various image augmentations based on real-world scenarios. This data set is the
first publicly available set in OMR research with sufficient size to train and
evaluate deep learning models. With the introduced augmentations a pitch
recognition accuracy of 81% and a duration accuracy of 94% is achieved,
resulting in a note level accuracy of 80%. Finally, the model is compared to
commercially available methods, showing a large improvements over these
applications.Comment: ISMIR 201
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