Deep Learning Techniques for Music Generation

Random News

Deep Learning Techniques for Music Generation
Triune Digital VHS CD TAPE SFX WAV
<<<<<<<<<<< 19-05-2021, 18:17 >>>>>>>>>>
Triune Digital VHS CD TAPE SFX WAV
FANTASTiC | 19 May 2021 | 1.8 GB With old video and audio equipment getting harder and harder to find these…

GUNBOI Wavelights Loop Kit WAV
<<<<<<<<<<< 28-06-2021, 12:37 >>>>>>>>>>
GUNBOI Wavelights Loop Kit WAV
FANTASTiC | 28 June 2021 | 76 MB 18 Sample loops curated, composed and mixed by GUNBOI. All samples have true…

Freak Music Chilled MIDI Lines WAV MiDi
<<<<<<<<<<< 14-05-2021, 09:30 >>>>>>>>>>
Freak Music Chilled MIDI Lines WAV MiDi
Team DECiBEL | 14 May 2021 | 264.4 MB Freak Music is proud to present the "Chilled MIDI Lines" - a collection…

Sacaii x Trvpyyy LIL SACHI n TRVP WAV
<<<<<<<<<<< 24-07-2021, 10:26 >>>>>>>>>>
Sacaii x Trvpyyy LIL SACHI n TRVP WAV
FANTASTiC | 24 July 2021 | 10 MB 10 Original Samples composed by SACAII x TRVPYYY

Archive

July 2021 (626)
June 2021 (630)
May 2021 (814)
April 2021 (788)
March 2021 (833)
February 2021 (781)
13-07-2021, 13:29

Deep Learning Techniques for Music Generation

Deep Learning Techniques for Music Generation

English | ISBN: 3319701622 | 2019 | 284 pages | PDF | 10 MB

This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure.

The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.

home page:
https://bit.ly/3edbRYw


DOWNLOAD

You like the news? Please share this news in social networks



Related News:

Computational Phonogram Archiving (Current Research in Systematic Musicology)Computational Phonogram Archiving (Current Research in Systematic Musicology)

2019 | ISBN: 3030026949 | English | 350 pages | PDF | 13 MB The future of music archiving and search engines lies in deep learning and big data. Music information retrieval algorithms automatically analyze musical features like timbre, melody, rhythm or musical form, and artificial intelligence then sorts and relates these features. At the first International Symposium on Computational...
Sonic Writing Technologies of Material Symbolic and Signal InscriptionsSonic Writing Technologies of Material Symbolic and Signal Inscriptions

English | 2019 | ISBN: 150131386X | 305 Pages | PDF | 5 MB Sonic Writing explores how contemporary music technologies trace their ancestry to previous forms of instruments and media. Studying the domains of instrument design, musical notation, and sound recording under the rubrics of material, symbolic, and signal inscriptions of sound, the book describes how these historical techniques of sonic...
Connectionist Representations of Tonal Music Discovering Musical Patterns by Interpreting Artifical Neural NetworksConnectionist Representations of Tonal Music Discovering Musical Patterns by Interpreting Artifical Neural Networks

English | 2018 | ISBN: 1771992204 | 312 Pages | PDF | 1.46 MB Previously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks...
Fundamentals of Music: Rudiments, Musicianship, and Composition (6th Edition)Fundamentals of Music: Rudiments, Musicianship, and Composition (6th Edition)

ISBN-13: 978-0205118335 | 150.32 MB Fundamentals of Music provides a clear and comprehensive approach to mastering the language of music. The authors invite students to create composition projects, develop aural skills through listening exercises, and analyze musical examples from various styles and genres....

  Views: 71
Views: 71

- THANKS FROM THE USERS -

Nobody said thanks, but you can be first!

Comments for Deep Learning Techniques for Music Generation:

No comments yet, add a comment!

Information

Would you like to leave your comment? Please Login to your account to leave comments. Don't have an account? You can create a free account now.

Member Login


Social Networking Login: