Special Issue "Statistical Models for Music Prediction and Generation"
Deadline for manuscript submissions: 31 December 2020.
Interests: music analysis; music generation; statistical models of music; pattern discovery
Music generation was one of the earliest applications of computing and machine learning, and much of the early work in music informatics was concerned with computational style emulation. Nearly 70 years ago, Shannon (“Prediction and Entropy of Printed English”, 1951) set out the topics of n-grams, long-range statistics, and entropy of a language, and this work led directly to pioneering research in music generation. In parallel music theoretical advances, Meyer (“Meaning in Music and Information Theory”, 1957) showed that aspects of information theory are highly relevant for music analysis. In recent decades, music prediction and generation by sampling from statistical models has been revisited (Conklin, “Music Generation from Statistical Models”, 2003), and continued advances in learning methods and algorithms have opened a new expanding era of music generation research. This Special Issue welcomes papers on the latest advances in music generation based on statistical modeling of music corpora.
Prof. Dr. Darrell Conklin
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Algorithms for sampling from predictive models of music
- Language models and deep learning
- Novel model architectures for music prediction
- Learning from small or heterogeneous corpora
- Template transformation methods
- Generation of pop tunes, polyphony, folk tunes, etc.
- Latent representations and embedding
- Multilayer textures: joint models of harmony, rhythm, and melody
- Information flow, cognitive expectation and surprise
- Semiotic structure and coherence: musical repetition, segmentation, and structuring